diff --git a/.gitignore b/.gitignore
index aabbe01f..d3dd2cbe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@
 /doc/build
 /docs/build
 /docs/source/_autosummary
+/build
 .debug
 .profiling
 .vscode
diff --git a/bluecellulab/__init__.py b/bluecellulab/__init__.py
index 01ac616d..e90686c3 100644
--- a/bluecellulab/__init__.py
+++ b/bluecellulab/__init__.py
@@ -9,7 +9,7 @@
 
 from bluecellulab.importer import import_hoc
 from .verbosity import *
-from .cell import Cell, create_ball_stick  # NOQA
+from .cell import Cell, create_ball_stick, MorphIOWrapper  # NOQA
 from .circuit import EmodelProperties
 from .connection import Connection  # NOQA
 from .plotwindow import PlotWindow  # NOQA
diff --git a/bluecellulab/cell/__init__.py b/bluecellulab/cell/__init__.py
index 022a9798..6a1212e6 100644
--- a/bluecellulab/cell/__init__.py
+++ b/bluecellulab/cell/__init__.py
@@ -4,3 +4,4 @@
 from .cell_dict import CellDict
 from .sonata_proxy import SonataProxy
 from .ballstick import create_ball_stick
+from .morphio_wrapper import MorphIOWrapper
diff --git a/bluecellulab/cell/core.py b/bluecellulab/cell/core.py
index 40596285..718ad018 100644
--- a/bluecellulab/cell/core.py
+++ b/bluecellulab/cell/core.py
@@ -87,7 +87,10 @@ def __init__(self,
 
         Args:
             template_path: Path to hoc template file.
-            morphology_path: Path to morphology file.
+            morphology_path: Path to morphology file. Supports .asc, .swc, .h5 and .h5 containers formats.
+                            If the morphology is in an H5 container, the path should be the
+                            path to the morphology file in the H5 container.
+                            For example: "merged-morphologies.h5/C4095O94"
             cell_id: ID of the cell, used in RNG seeds.
             record_dt: Timestep for the recordings.
             template_format: Cell template format such as 'v5' or 'v6_air_scaler'.
diff --git a/bluecellulab/cell/morphio_wrapper.py b/bluecellulab/cell/morphio_wrapper.py
new file mode 100644
index 00000000..badfa146
--- /dev/null
+++ b/bluecellulab/cell/morphio_wrapper.py
@@ -0,0 +1,404 @@
+"""A wrapper for MorphIO objects.
+
+Provides additional neuron features on top of MorphIO basic morphology
+handling. Adapted from neurodamus/morphio_wrapper.py
+"""
+
+import logging
+import os
+from dataclasses import dataclass
+
+import numpy as np
+from numpy.linalg import eig, norm
+
+from bluecellulab.exceptions import BluecellulabError
+
+logger = logging.getLogger(__name__)
+"""[START] Implementations retrieved from nse/morph-tool (!= hpc/morpho-tool
+!!!) These functions are needed for soma points computation (a la import3d).
+
+TODO: Once we have something stable, integrate nse/morph-tool
+"""
+X, Y, R = 0, 1, 3
+
+
+def split_morphology_path(morphology_path):
+    """Split `{collection_path}/{morph_name}.{ext}`"""
+    if os.path.exists(morphology_path):
+        collection_path = os.path.dirname(morphology_path)
+        morph_name, morph_ext = os.path.splitext(os.path.basename(morphology_path))
+        return collection_path, morph_name, morph_ext
+
+    collection_path = morphology_path
+    while not os.path.exists(collection_path):
+        if collection_path == os.path.dirname(collection_path):
+            raise BluecellulabError("Failed to split path.")
+        collection_path = os.path.dirname(collection_path)
+
+    morph_name, morph_ext = os.path.splitext(os.path.relpath(morphology_path, collection_path))
+
+    return collection_path, morph_name, morph_ext
+
+
+def contourcenter(xyz):
+    """Python implementation of NEURON code: lib/hoc/import3d/import3d_sec.hoc"""
+    POINTS = 101
+
+    points = np.vstack((np.diff(xyz[:, [X, Y]], axis=0), xyz[0, [X, Y]]))
+    perim = np.cumsum(np.hstack(((0,), norm(points, axis=1))))[:-1]
+
+    d = np.linspace(0, perim[-1], POINTS)
+    new_xyz = np.zeros((POINTS, 3))
+    for i in range(3):
+        new_xyz[:, i] = np.interp(x=d, xp=perim, fp=xyz[:, i])
+
+    mean = np.mean(new_xyz, axis=0)
+
+    return mean, new_xyz
+
+
+def get_sides(points, major, minor):
+    """Circular permutation of the points so that the point with the largest
+    coordinate along the major axis becomes the last point tobj =
+    major.c.mul(d.x[i])  ###### uneeded?
+
+    line 1191
+    """
+    major_coord, minor_coord = np.dot(points, major), np.dot(points, minor)
+
+    imax = np.argmax(major_coord)
+    # pylint: disable=invalid-unary-operand-type
+    major_coord, minor_coord = (np.roll(major_coord, -imax), np.roll(minor_coord, -imax))
+
+    imin = np.argmin(major_coord)
+
+    sides = [major_coord[:imin][::-1], major_coord[imin:]]
+    rads = [minor_coord[:imin][::-1], minor_coord[imin:]]
+    return sides, rads
+
+
+def make_convex(sides, rads):
+    """Keep only points that make path convex."""
+
+    def convex_idx(m):
+        """Return index to elements of m that make it convex.
+
+        Note: not efficient at the moment
+        # now we have the two sides without the min and max points (rads[0]=0)
+        # we hope both sides now monotonically increase, i.e. convex
+        # make it convex
+        """
+        idx = np.ones_like(m, dtype=bool)
+        last_val = m[-1]
+        for i in range(len(m) - 2, -1, -1):
+            if m[i] < last_val:
+                last_val = m[i]
+            else:
+                idx[i] = False
+        return idx
+
+    for i_side in [0, 1]:
+        ci = convex_idx(sides[i_side])
+        sides[i_side] = sides[i_side][ci]
+        rads[i_side] = rads[i_side][ci]
+    return sides, rads
+
+
+def contour2centroid(mean, points):
+    """This follows the function in lib/hoc/import3d/import3d_gui.hoc most of
+    the comments are from there, so if you want to follow along, it should
+    break up the function the same way."""
+    logging.debug("Converting soma contour into a stack of cylinders")
+
+    # find the major axis of the ellipsoid that best fits the shape
+    # assuming (falsely in general) that the center is the mean
+
+    points -= mean
+    eigen_values, eigen_vectors = eig(np.dot(points.T, points))
+
+    # To be consistent with NEURON eigen vector directions
+    eigen_vectors *= -1
+
+    idx = np.argmax(eigen_values)
+    major = eigen_vectors[:, idx]
+    # minor is normal and in xy plane
+    idx = 3 - np.argmin(eigen_values) - np.argmax(eigen_values)
+    minor = eigen_vectors[:, idx]
+    minor[2] = 0
+
+    sides, rads = get_sides(points, major, minor)
+    sides, rads = make_convex(sides, rads)
+
+    tobj = np.sort(np.hstack(sides))
+    new_major_coord = np.linspace(tobj[1], tobj[-2], 21)
+    rads[0] = np.interp(new_major_coord, sides[0], rads[0])
+    rads[1] = np.interp(new_major_coord, sides[1], rads[1])
+
+    points = major * new_major_coord[:, np.newaxis] + mean
+    diameters = np.abs(rads[0] - rads[1])
+
+    # avoid 0 diameter ends
+    diameters[0] = np.mean(diameters[:2])
+    diameters[-1] = np.mean(diameters[-2:])
+
+    return points, diameters
+
+
+def _to_sphere(neuron):
+    """Convert a 3-pts cylinder or a 1-pt sphere into a circular contour that
+    represents the same sphere."""
+    radius = neuron.soma.diameters[0] / 2.0
+    N = 20
+    points = np.zeros((N, 3))
+    phase = 2 * np.pi / (N - 1) * np.arange(N)
+    points[:, 0] = radius * np.cos(phase)
+    points[:, 1] = radius * np.sin(phase)
+    points += neuron.soma.points[0]
+    neuron.soma.points = points
+    neuron.soma.diameters = np.repeat(radius, N)
+
+
+def single_point_sphere_to_circular_contour(neuron):
+    """Transform a single point soma that represents a sphere into a circular
+    contour that represents the same sphere."""
+    logging.debug(
+        "Converting 1-point soma (sperical soma) to circular contour representing the same sphere"
+    )
+    _to_sphere(neuron)
+
+
+"""
+    [END] Implementations retrieved from nse/morph-tool (!= hpc/morpho-tool !!!)
+"""
+
+
+@dataclass
+class SectionName:
+    """A simple container to uniquely identify a NEURON Section by name and ID.
+
+    Attributes:
+        name (str): The name of the section (e.g., "soma", "dend", etc.).
+                    This corresponds to the section's logical type or label.
+        id (int): The index of the section among all sections with the same name.
+                  For example, in a list of dendrites, this would identify
+                  dend[0], dend[1], etc.
+
+    Example:
+        For NEURON's `soma[0]`, the corresponding SectionName would be:
+
+            SectionName(name="soma", id=0)
+
+        This allows unique referencing even in models where multiple sections
+        have the same base name.
+    """
+
+    name: str
+    id: int
+
+    def __str__(self):
+        return f"{self.name}[{self.id}]"
+
+
+class MorphIOWrapper:
+    """A class that wraps a MorphIO object and gets everything ready for HOC
+    usage."""
+
+    morph = property(lambda self: self._morph)
+
+    def __init__(self, input_file, options=0):
+        self._collection_dir, self._morph_name, self._morph_ext = split_morphology_path(input_file)
+        self._options = options
+        self._build_morph()
+        # This logic is similar to what's in BaseCell, but at this point we are still
+        # constructing the cell, so we don't yet have access to a fully initialized instance.
+        # Therefore, we cannot reuse the BaseCell implementation directly and need
+        # a custom solution here.
+        self._section_names = self._get_section_names()
+        self._build_sec_typeid_distrib()
+
+    def _build_morph(self):
+        """Build immutable morphology, going trough mutable and applying neuron
+        adjustemnts."""
+        try:
+            # Lazy import morphio since it has an issue with execl
+            from morphio import Collection, Morphology, Option, SomaType
+        except ImportError as e:
+            raise RuntimeError("MorphIO is not available") from e
+
+        collection = Collection(self._collection_dir, extensions=[self._morph_ext])
+        options = self._options | Option.nrn_order
+        self._morph = collection.load(self._morph_name, options, mutable=True)
+
+        # Re-compute the soma points as they are computed in import3d_gui.hoc
+        if self._morph.soma_type not in {SomaType.SOMA_SINGLE_POINT, SomaType.SOMA_SIMPLE_CONTOUR}:
+            msg = f"H5 morphology is not supposed to have a soma of type: {self._morph.soma_type}"
+            raise Exception(msg)
+        logging.debug(
+            "(%s, %s, %s) has soma type : %s",
+            self._collection_dir,
+            self._morph_name,
+            self._morph_ext,
+            self._morph.soma_type,
+        )
+
+        if self._morph.soma_type == SomaType.SOMA_SINGLE_POINT:
+            """ See NRN import3d_gui.hoc -> instantiate()
+                            -> sphere_rep(xx, yy, zz, dd) """
+            single_point_sphere_to_circular_contour(self._morph)
+        elif self._morph.soma_type == SomaType.SOMA_SIMPLE_CONTOUR:
+            """ See NRN import3d_gui.hoc -> instantiate()
+                            -> contour2centroid(xx, yy, zz, dd, sec) """
+            mean, new_xyz = contourcenter(self._morph.soma.points)
+            self._morph.soma.points, self._morph.soma.diameters = contour2centroid(mean, new_xyz)
+
+        self._morph = Morphology(self._morph)
+
+    def _get_section_names(self) -> list[SectionName]:
+        """Returns a list of SectioName.
+
+        Relative_index is the index of the section within its type
+        group, as expected by the NEURON simulator. NEURON organizes
+        mechanisms and pointers (e.g., for synapses or diffusion) into
+        arrays grouped by section type (e.g., axon, dendrite), so this
+        relative index identifies the section position within its group.
+        The list starts with ('soma', 0).
+        """
+        result = [SectionName("soma", 0)]
+
+        last_type = None
+        type_start_index = 0
+
+        for i, sec in enumerate(self._morph.sections, start=1):
+            sec_type = self._morph.section_types[sec.id]
+
+            if sec_type != last_type:
+                last_type = sec_type
+                type_start_index = i
+
+            relative_index = i - type_start_index
+            result.append(SectionName(MorphIOWrapper.type2name(sec_type), relative_index))
+
+        return result
+
+    def _build_sec_typeid_distrib(self):
+        """Build typeid distribution on top of MorphIO section_types."""
+        """This will hold np.array that will map the different type ids to the
+        start id wrt to section_types and their count. For example, axon
+        type(2) starts at section.id 0 and and totals 2724 sections.
+
+                | type_id | start_id  |   count   |
+                | ------- | --------- | --------- |
+                |      2  |        0  |     2724  |
+                |      3  |     2724  |       75  |
+
+
+            >>> _sec_typeid_distrib
+            array([[(2,    0, 2724)],
+                   [(3, 2724,   75)]],
+                  dtype=[('type_id', '<i8'), ('start_id', '<i8'), ('count', '<i8')])
+
+            Then use it like this:
+            >>> _sec_typeid_distrib[['type_id', 'start_id']]
+            array([[(2,    0)],
+                   [(3, 2724)]],
+                  dtype={'names':['type_id','start_id'],....}
+        """
+        self._sec_typeid_distrib = np.dstack(
+            np.unique(self._morph.section_types, return_counts=True, return_index=True)
+        )[0]
+        self._sec_typeid_distrib = np.concatenate(([(1, -1, 1)], self._sec_typeid_distrib), axis=0)
+        self._sec_typeid_distrib.dtype = [("type_id", "<i8"), ("start_id", "<i8"), ("count", "<i8")]
+
+    def morph_as_hoc(self):
+        """Uses morphio object to read and generate hoc commands just like
+        import3d_gui.hoc."""
+        cmds = []
+        """We need to get the number of sections for each type in order to
+        generate the create commands.
+
+        E.g.: ( soma , 1  ) ( dend , 52 ) ( axon , 23 ) ( apic , 5  )
+        """
+        # generate create commands
+        for [(type_id, count)] in self._sec_typeid_distrib[["type_id", "count"]]:
+            tstr = self.type2name(type_id)
+            tstr1 = f"create {tstr}[{count}]"
+            cmds.append(tstr1)
+            tstr1 = self.mksubset(type_id, tstr)
+            cmds.append(tstr1)
+
+        cmds.append("forall all.append")
+
+        # generate 3D soma points commands. Order is reversed wrt NEURON's soma points.
+        cmds.extend(
+            (
+                f"soma {{ pt3dadd({p[0]:.8g}, {p[1]:.8g}, {p[2]:.8g}, {d:.8g}) }}"
+                for p, d in zip(
+                    reversed(self._morph.soma.points),
+                    reversed(self._morph.soma.diameters),
+                    strict=True,
+                )
+            )
+        )
+
+        # generate sections connect + their respective 3D points commands
+        for i, sec in enumerate(self._morph.sections):
+            index = i + 1
+            tstr = self._section_names[index]
+
+            if not sec.is_root:
+                if sec.parent is not None:
+                    parent_index = sec.parent.id + 1
+                    tstr1 = self._section_names[parent_index]
+                    tstr1 = f"{tstr1} connect {tstr}(0), {1}"
+                    cmds.append(tstr1)
+            else:
+                tstr1 = f"soma connect {tstr}(0), {0.5}"
+                cmds.append(tstr1)
+
+                # pt3dstyle does not impact simulation numbers. This will be kept for x-reference.
+                # tstr1 = "{} {{ pt3dstyle(1, {:.8g}, {:.8g}, {:.8g}) }}".format
+                #                   (tstr, mean[0], mean[1], mean[2])
+                # cmds.append(tstr1)
+
+            # 3D point info
+            cmds.extend(
+                f"{tstr} {{ pt3dadd({p[0]:.8g}, {p[1]:.8g}, {p[2]:.8g}, {d:.8g}) }}"
+                for p, d in zip(sec.points, sec.diameters, strict=True)
+            )
+
+        return cmds
+
+    """
+         [START] Python versions of import3d_gui.hoc helper functions
+         Note: nrn function names will be kept for reference
+    """
+
+    _type2name_dict = {1: "soma", 2: "axon", 3: "dend", 4: "apic"}
+
+    @classmethod
+    def type2name(cls, type_id):
+        """:param type_id: id of section type
+        :return: name representation of the section type.
+
+        If not found in _type2name_dict, then default is:
+        if (type < 0): "minus_{}".format(-type)
+        else: "dend_{}".format(type)
+        """
+        return cls._type2name_dict.get(type_id) or (
+            f"minus_{-type_id}" if type_id < 0 else f"dend_{type_id}"
+        )
+
+    _mksubset_dict = {1: "somatic", 2: "axonal", 3: "basal", 4: "apical"}
+
+    @classmethod
+    def mksubset(cls, type_id, type_name):
+        """:param type_id: id of section type
+        :param type_name: the name of the section type
+        :return: command to append section type to subset
+        """
+        tstr = cls._mksubset_dict.get(type_id) or (
+            f"minus_{-type_id}set" if type_id < 0 else f"dendritic_{type_id}"
+        )
+
+        tstr1 = f'forsec "{type_name}" {tstr}.append'
+        return tstr1
diff --git a/bluecellulab/cell/template.py b/bluecellulab/cell/template.py
index 1df1610e..221128e9 100644
--- a/bluecellulab/cell/template.py
+++ b/bluecellulab/cell/template.py
@@ -67,7 +67,9 @@ def __init__(
 
         if not os.path.exists(template_filepath):
             raise FileNotFoundError(f"Couldn't find template file: {template_filepath}")
-        if not os.path.exists(morph_filepath):
+
+        # Check morphology path - handle H5 container paths
+        if not self._is_valid_morphology_path(morph_filepath):
             raise FileNotFoundError(f"Couldn't find morphology file: {morph_filepath}")
 
         self.template_name = self.load(template_filepath)
@@ -77,7 +79,29 @@ def __init__(
 
     def get_cell(self, gid: Optional[int]) -> HocObjectType:
         """Returns the hoc object matching the template format."""
-        morph_dir, morph_fname = os.path.split(self.morph_filepath)
+        # Use split_morphology_path for consistent handling of all morphology types
+        # including H5 containers (similar to Neurodamus approach)
+        from bluecellulab.cell.morphio_wrapper import split_morphology_path
+
+        morph_dir, morph_name, morph_ext = split_morphology_path(str(self.morph_filepath))
+
+        # Reconstruct filename with extension (Neurodamus-style)
+        # For H5 containers: morph_dir ends with .h5, morph_ext might be wrong due to dots in cell name
+        # For single files: morph_name is "file" and morph_ext is ".h5"
+        if morph_dir.endswith('.h5'):
+            # This is an H5 container path - reconstruct the cell name and add .h5
+            # The cell name is everything after the container file
+            morph_fname = morph_name + morph_ext if morph_ext and not morph_ext.startswith('.') else morph_name + morph_ext
+            # Ensure it ends with .h5 for HOC template validation
+            if not morph_fname.endswith('.h5'):
+                morph_fname = morph_fname + '.h5'
+        elif morph_ext:
+            # Regular file with extension
+            morph_fname = morph_name + morph_ext
+        else:
+            # Fallback: add .h5 extension
+            morph_fname = morph_name + '.h5'
+
         if self.template_format == "v6":
             attr_names = getattr(
                 neuron.h, self.template_name.split('_bluecellulab')[0] + "_NeededAttributes", None
@@ -145,6 +169,45 @@ def load(self, template_filename: str) -> str:
 
         return template_name
 
+    def _is_valid_morphology_path(self, morph_filepath: str) -> bool:
+        """Check if morphology path is valid, handling H5 container paths."""
+        # Check if it's a regular file
+        if os.path.exists(morph_filepath):
+            return True
+
+        # Check if it's an H5 container path (format: container.h5/CellName.h5)
+        if '/' in morph_filepath and '.h5' in morph_filepath:
+            # Split into container path and cell name
+            parts = morph_filepath.split('/')
+            if len(parts) >= 2:
+                # Find the part that ends with .h5 and is not the last part
+                # (last part is the cell name which also ends with .h5)
+                h5_index = -1
+                for i, part in enumerate(parts[:-1]):  # Exclude last part
+                    if part.endswith('.h5'):
+                        h5_index = i
+                        break
+
+                if h5_index >= 0:
+                    # The container file is everything up to and including the .h5 part
+                    container_path = '/'.join(parts[:h5_index + 1])
+                    # Cell name is everything after, with .h5 extension removed
+                    cell_name_with_ext = '/'.join(parts[h5_index + 1:])
+                    # Remove .h5 extension from cell name for H5 container lookup
+                    cell_name = cell_name_with_ext.rsplit('.h5', 1)[0] if cell_name_with_ext.endswith('.h5') else cell_name_with_ext
+
+                    # Check if container file exists
+                    if os.path.exists(container_path):
+                        try:
+                            import h5py
+                            # Check if cell exists in the container
+                            with h5py.File(container_path, 'r') as f:
+                                return cell_name in f
+                        except Exception:
+                            return False
+
+        return False
+
 
 def shorten_and_hash_string(label: str, keep_length=40, hash_length=9) -> str:
     """Converts a string to a shorter string if required.
diff --git a/bluecellulab/circuit/circuit_access/sonata_circuit_access.py b/bluecellulab/circuit/circuit_access/sonata_circuit_access.py
index a76f4a04..e1a47f8d 100644
--- a/bluecellulab/circuit/circuit_access/sonata_circuit_access.py
+++ b/bluecellulab/circuit/circuit_access/sonata_circuit_access.py
@@ -11,6 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Sonata circuit access implementation for BlueCelluLab."""
+
 from __future__ import annotations
 import hashlib
 from functools import lru_cache
@@ -259,8 +261,20 @@ def is_valid_group(self, group: str) -> bool:
 
     @lru_cache(maxsize=16)
     def get_target_cell_ids(self, target: str) -> set[CellId]:
-        ids = self._circuit.nodes.ids(self.config.impl.node_sets[target])
-        return {CellId(x.population, x.id) for x in ids}
+        # Use merged node_sets from simulation config (includes both circuit and simulation node_sets)
+        node_sets = self.config.get_node_sets()
+        node_set_def = node_sets[target]
+
+        # If node_set specifies a population, query only that population to avoid
+        # "Invalid range: 0-0" errors when other populations are empty
+        if isinstance(node_set_def, dict) and "population" in node_set_def:
+            population = node_set_def["population"]
+            ids = self._circuit.nodes[population].ids(node_set_def)
+            return {CellId(population, x) for x in ids}
+        else:
+            # For node_sets without population filter, query all populations
+            ids = self._circuit.nodes.ids(node_set_def)
+            return {CellId(x.population, x.id) for x in ids}
 
     @lru_cache(maxsize=100)
     def fetch_cell_info(self, cell_id: CellId) -> pd.Series:
@@ -297,14 +311,55 @@ def get_cell_ids_of_targets(self, targets: list[str]) -> set[CellId]:
         return cell_ids
 
     def morph_filepath(self, cell_id: CellId) -> str:
-        """Returns the .asc morphology path from 'alternate_morphologies'."""
+        """Returns the morphology path from 'alternate_morphologies' based on
+        available formats."""
         node_population = self._circuit.nodes[cell_id.population_name]
-        try:  # if asc defined in alternate morphology
-            return str(node_population.morph.get_filepath(cell_id.id, extension="asc"))
-        except BluepySnapError as e:
-            logger.debug(f"No asc morphology found for {cell_id}, trying swc.")
+
+        # Get the alternate morphologies configuration
+        alternate_morphologies = node_population.config.get("alternate_morphologies")
+
+        # Check for H5v1 format first (highest priority for H5 containers)
+        if alternate_morphologies and "h5v1" in alternate_morphologies:
+            h5_container_path = alternate_morphologies["h5v1"]
+            # Get the morphology name for this cell
+            cell_properties = node_population.get(cell_id.id)
+            morphology_name = cell_properties.get("morphology", "")
+            if morphology_name:
+                # Return the H5 container path with cell name and .h5 extension
+                # This format works with os.path.split() in NeuronTemplate.get_cell()
+                # which splits it into (container.h5, CellName.h5) for the template
+                return f"{h5_container_path}/{morphology_name}.h5"
+
+        # Check for neurolucida-asc format
+        if alternate_morphologies and "neurolucida-asc" in alternate_morphologies:
+            try:
+                return str(node_population.morph.get_filepath(cell_id.id, extension="asc"))
+            except BluepySnapError:
+                logger.debug(f"Could not get ASC morphology from alternate_morphologies for {cell_id}")
+
+        # Fallback to default morphology handling
+        try:
             return str(node_population.morph.get_filepath(cell_id.id))
+        except BluepySnapError as e:
+            raise BluepySnapError(f"Could not determine morphology path for cell {cell_id}: {e}")
 
     def emodel_path(self, cell_id: CellId) -> str:
         node_population = self._circuit.nodes[cell_id.population_name]
-        return str(node_population.models.get_filepath(cell_id.id))
+
+        # Try to use the models helper if available
+        try:
+            return str(node_population.models.get_filepath(cell_id.id))
+        except BluepySnapError:
+            # If models helper is not available, construct path from model_template
+            # model_template format is typically "hoc:template_name"
+            cell_properties = node_population.get(cell_id.id)
+            model_template = cell_properties.get("model_template", "")
+
+            if model_template.startswith("hoc:"):
+                template_name = model_template.split(":", 1)[1]
+                # Get the biophysical_neuron_models_dir from config
+                models_dir = node_population.config.get("biophysical_neuron_models_dir", "")
+                if models_dir:
+                    return str(Path(models_dir) / f"{template_name}.hoc")
+
+            raise BluepySnapError(f"Could not determine emodel path for cell {cell_id}")
diff --git a/bluecellulab/hoc/Cell.hoc b/bluecellulab/hoc/Cell.hoc
index 129f2dbb..5cc581f2 100644
--- a/bluecellulab/hoc/Cell.hoc
+++ b/bluecellulab/hoc/Cell.hoc
@@ -22,6 +22,7 @@
 {load_file("stdlib.hoc")}
 {load_file("import3d.hoc")}
 {load_file("fileUtils.hoc")}
+{load_file("MorphIO.hoc")}
 
 
 // In case in SimConfig there's a new extension registered, pass that info to Cell to attempt lo load it
@@ -126,10 +127,9 @@ proc AddMorph(){ localobj morph, import, strobj, morphPath, extension, global_ex
     }
 
     if( strcmp(extension.s, "h5") == 0 ) {
-        if( nrnpython("import morphio_wrapper") == 0 ) {
-            terminate( ".h5 morphlogy used but cannot load 'morphio_wrapper.py' from neurodamus-core" )
-        }
-        AddHocMorph(pyobj.morphio_wrapper.MorphIOWrapper(morphPath.s).morph_as_hoc())
+        morphio_read(this, morphPath.s)
+        indexSections()
+        geom_nsec()
         return
     }
 
diff --git a/bluecellulab/hoc/MorphIO.hoc b/bluecellulab/hoc/MorphIO.hoc
new file mode 100644
index 00000000..63c1ed33
--- /dev/null
+++ b/bluecellulab/hoc/MorphIO.hoc
@@ -0,0 +1,35 @@
+/**
+ * @file MorphIO.hoc
+ * @brief MorphIO wrapper handler for morphology-loading.
+ * @author BlueCelluLab Team
+ * @date 2026
+ * @remark Morphology loading for H5 files using MorphIO
+ * Adapted from neurodamus/data/hoc/MorphIO.hoc
+ */
+
+objref pyobj
+pyobj = new PythonObject()
+
+/**
+ *  Execute hoc commands in the cell's context.
+ * @param $o1 Cell object to execute commands in
+ * @param $o2 python List holding commands
+ */
+proc execute_commands_from_pylist () { localobj cmds
+    cmds = $o2
+    for i = 0, pyobj.len(cmds) - 1 {
+        execute(cmds._[i], $o1)
+    }
+}
+
+/**
+ *  Load hoc-based morphology.
+ * @param $o1 Cell object to load morphology into
+ * @param $s2 Morphology file path
+ */
+proc morphio_read() { 
+    if( nrnpython("from bluecellulab.cell.morphio_wrapper import MorphIOWrapper") == 0 ) {
+        terminate( "Cannot load 'MorphIOWrapper' from bluecellulab" )
+    }
+    execute_commands_from_pylist($o1, pyobj.MorphIOWrapper($s2).morph_as_hoc())
+}
diff --git a/bluecellulab/importer.py b/bluecellulab/importer.py
index fcc7815f..a9b07f2e 100644
--- a/bluecellulab/importer.py
+++ b/bluecellulab/importer.py
@@ -59,6 +59,7 @@ def import_hoc(neuron: ModuleType) -> None:
         "Cell.hoc",  # ND
         "TDistFunc.hoc",  # ND, test dependency
         "TStim.hoc",  # ND
+        "MorphIO.hoc",  # H5 morphology support (loaded after Cell.hoc)
     ]
 
     for hoc_file in hoc_files:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 95a90826..836d0b91 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -20,6 +20,7 @@
     "efel",
     "neo",
     "quantities",
+    "morphio",
 ]
 
 suppress_warnings = [
diff --git a/examples/1-singlecell/singlecell_H5_morph_H5_container.ipynb b/examples/1-singlecell/singlecell_H5_morph_H5_container.ipynb
new file mode 100644
index 00000000..7c7c1486
--- /dev/null
+++ b/examples/1-singlecell/singlecell_H5_morph_H5_container.ipynb
@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bdcde5d7",
+   "metadata": {},
+   "source": [
+    "## Simulate Single Cell Model with H5 Morphology and H5 Morphology Containers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f011909",
+   "metadata": {},
+   "source": [
+    "This is an advanced example. Please refer to [singlecell.ipynb notebook](singlecell.ipynb) for a basic example.Morphology formats that can be run by BlueCelluLab are: [asc](https://morphio.readthedocs.io/en/latest/specification_neurolucida.html#id1), [swc](https://swc-specification.readthedocs.io/en/latest/swc.html), [H5](https://morphology-documentation.readthedocs.io/en/latest/h5v1.html#file-format) and [H5 container](https://morphology-documentation.readthedocs.io/en/latest/h5-containers.html).\n",
+    "\n",
+    "You can read more about the morphology formats [here](https://morphio.readthedocs.io/en/latest/specification.html). This notebook demonstrates run single cell models using different morphology formats:\n",
+    "1. [H5](https://morphology-documentation.readthedocs.io/en/latest/h5v1.html#file-format)  \n",
+    "2. [H5 container](https://morphology-documentation.readthedocs.io/en/latest/h5-containers.html).\n",
+    "\n",
+    "\n",
+    "Both H5 and H5 container morphology formats take less space to store on a disk e.g. A single H5 container file can contain millions of morphologies in a single H5 file. They are used for storing large circuit morphologies with millions of morphologies of neurons. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fa091d5",
+   "metadata": {},
+   "source": [
+    "The morphologies of this rat Somatosensory cortex circuit are stored in H5 container format [Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation)](https://doi.org/10.7554/eLife.99693.3). You can view the central subvolume of this nbS1 circuit (nbS1-O1) on your own [Virtual Labs](https://www.openbraininstitute.org/app/virtual-lab) of the [Open Brain Insititute](https://www.openbraininstitute.org/)(OBI)\n",
+    "This nbS1-O1 and nbS1 circuit use morphologies in H5 container morphology format. Once you login, you can view all the details, subcircuits and circuit analysis on the detailed circuit page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2872a68",
+   "metadata": {},
+   "source": [
+    "**[Click here to open nbS1-O1 Circuit in your Virtual Lab](https://www.openbraininstitute.org/app/entity/96203ca0-8fbf-4e3a-9c9e-b23309020109)**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1edea5b",
+   "metadata": {},
+   "source": [
+    "For this notebook, we will use a `cADpyr_L5TPC` single cell model used in the circuit with all the intrinsic and extrinsic synapses extracted from the Somatosensory cortex(SSCx) circuit publish in [Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation](https://doi.org/10.7554/eLife.99693.3).We also call such single cells with all its synapses, a **Synaptome**. The synaptome is stored in the [SONATA](https://sonata-extension.readthedocs.io/en/latest/) format. You can also view the cell in the [Open Brain Institute's Virtual Labs](https://www.openbraininstitute.org/app/virtual-lab):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20f817c9",
+   "metadata": {},
+   "source": [
+    "**[Click here to open nbS1-O1__202247__cADpyr__L5_TPC_A in your Virtual Lab](https://www.openbraininstitute.org/app/entity/82bae430-aff9-46b1-8ce7-e616bc733ed2)**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ec26b93",
+   "metadata": {},
+   "source": [
+    "Locally, it is present in this folder:  `BlueCelluLab/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A`\n",
+    "The cell is stored in SONATA format with all the components including the H5 morphology. We also have the morphology in a H5 container containing a single morphology. SONATA circuit documentation: https://sonata-extension.readthedocs.io/en/latest/ "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1ba0064",
+   "metadata": {},
+   "source": [
+    "Let's first compile the ion channel mechanisms (mod files) using NEURON simulator's command line tool `nrnivmodl`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63849af6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First delete previous compiled mod files\n",
+    "! rm -r arm64 # for mac-based systems\n",
+    "! rm -r x86_64 # for linux-based systems\n",
+    "# Compile mod files\n",
+    "data_folder=\"../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A\"\n",
+    "!nrnivmodl $data_folder/mod\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b1f2202b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from bluecellulab import Cell, Simulation\n",
+    "from bluecellulab.circuit.circuit_access import EmodelProperties\n",
+    "from bluecellulab.simulation.neuron_globals import NeuronGlobals\n",
+    "import h5py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93264641",
+   "metadata": {},
+   "source": [
+    "Get the hoc file and H5 morpholgy location from the above data folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2d92a842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hoc_file = f\"{data_folder}/emodels_hoc/cADpyr_L5TPC.hoc\"\n",
+    "morph_file_single_h5 = f\"{data_folder}/morphologies/h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c740b1",
+   "metadata": {},
+   "source": [
+    "We will use the threahold current and holding current properties of the cell to initialise it. These are present in the nodes.h5 file of the SOANTA format. Let's read the nodes.h5 file and extract the threshold current and holding current properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2cf81441",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Threshold current: 0.24553125\n",
+      "Holding current: -0.09796987224035547\n"
+     ]
+    }
+   ],
+   "source": [
+    "nodes_file_path = f\"{data_folder}/S1nonbarrel_neurons/nodes.h5\"\n",
+    "nodes_file_data = h5py.File(nodes_file_path, \"r\")\n",
+    "\n",
+    "threshold_current=float(nodes_file_data['nodes/S1nonbarrel_neurons/0/dynamics_params/threshold_current'][0])\n",
+    "print(f\"Threshold current: {threshold_current}\")\n",
+    "holding_current=float(nodes_file_data['nodes/S1nonbarrel_neurons/0/dynamics_params/holding_current'][0])\n",
+    "print(f\"Holding current: {holding_current}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9fcbccd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emodel_properties = EmodelProperties(threshold_current=threshold_current,\n",
+    "                                     holding_current=holding_current,\n",
+    "                                     AIS_scaler=1.0)\n",
+    "cell = Cell(template_path=hoc_file, \n",
+    "            morphology_path=morph_file_single_h5, \n",
+    "            template_format=\"v6\", \n",
+    "            emodel_properties=emodel_properties)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3f2c63fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Voltage (mV)')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set initial voltage\n",
+    "ng = NeuronGlobals.get_instance()\n",
+    "ng.v_init = -80\n",
+    "\n",
+    "\n",
+    "sim = Simulation()\n",
+    "sim.add_cell(cell)\n",
+    "\n",
+    "# insert step current clamp of 1.25 nA \n",
+    "cell.add_step(start_time=20.0, stop_time=100.0, level=1.5) \n",
+    "sim.run(tstop=140, cvode=False)\n",
+    "time_single_h5_morphology, voltage_single_h5_morphology = cell.get_time(), cell.get_soma_voltage()\n",
+    "plt.plot(time_single_h5_morphology, voltage_single_h5_morphology)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Voltage (mV)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad3f4328",
+   "metadata": {},
+   "source": [
+    "### H5 Container Single Cell\n",
+    "\n",
+    "We use the same hoc file but this time use a morphology stored in an H5 container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "81cfcf9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/emodels_hoc/cADpyr_L5TPC.hoc'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hoc_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb664aec",
+   "metadata": {},
+   "source": [
+    "We give the morphology path as `\"merged-morphologies.h5/MorphName\"` where `MorphName` is the name of the morphology in the H5 container. NB: `MorphName` does not have an extension at the end.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c19af575",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HOC file:\n",
+      " ../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/emodels_hoc/cADpyr_L5TPC.hoc\n",
+      "Morphology container:\n",
+      " ../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/merged-morphologies.h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use H5 container format (correct way)\n",
+    "hoc_file = f\"{data_folder}/emodels_hoc/cADpyr_L5TPC.hoc\"\n",
+    "morph_h5_container = f\"{data_folder}/morphologies/merged-morphologies.h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0\"\n",
+    "\n",
+    "print(f\"HOC file:\\n {hoc_file}\")\n",
+    "print(f\"Morphology container:\\n {morph_h5_container}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "99f1b597",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emodel_properties = EmodelProperties(threshold_current=threshold_current,\n",
+    "                                     holding_current=holding_current,\n",
+    "                                     AIS_scaler=1.0)\n",
+    "cell_container = Cell(template_path = hoc_file, \n",
+    "            morphology_path = morph_h5_container, \n",
+    "            template_format=\"v6\", \n",
+    "            emodel_properties=emodel_properties)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78c0f8f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Voltage (mV)')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set initial voltage\n",
+    "ng = NeuronGlobals.get_instance()\n",
+    "ng.v_init = -80\n",
+    "\n",
+    "\n",
+    "sim = Simulation()\n",
+    "sim.add_cell(cell)\n",
+    "\n",
+    "# insert step current clamp of 1.25 nA \n",
+    "cell_container.add_step(start_time=20.0, stop_time=100.0, level=1.5) \n",
+    "sim.run(tstop=140, cvode=False)\n",
+    "time_container, voltage_container = cell_container.get_time(), cell_container.get_soma_voltage()\n",
+    "plt.plot(time_container, voltage_container)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Voltage (mV)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b6a5d30",
+   "metadata": {},
+   "source": [
+    "### Compare the two results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "29b727ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time_single_h5_morphology, voltage_single_h5_morphology, label=\"H5 morphology\", color=\"blue\", linestyle=\"-\")\n",
+    "plt.plot(time_container, voltage_container, label=\"H5 container\", color=\"red\", linestyle=\"-.\")\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Voltage (mV)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4632f135",
+   "metadata": {},
+   "source": [
+    "As you can see the results from cell initialization are the same for both H5 morphology and H5 container morphology."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75bbd572",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/2-sonata-network/run_circuit_mpi/README.md b/examples/2-sonata-network/run_circuit_mpi/README.md
new file mode 100644
index 00000000..b3693461
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/README.md
@@ -0,0 +1,67 @@
+# SONATA Circuit with H5 Container Morphologies
+
+## Introduction
+
+This directory contains an example of running a SONATA circuit simulation using BlueCelluLab with morphologies stored in H5 containers. See example 1 singlecell_H5_morph_H5_container.ipynb for more details of morphology formats.
+
+## Circuit Details
+For this example, we will use a `cADpyr_L5TPC` single cell [SONATA](https://sonata-extension.readthedocs.io/en/latest/) model used in the circuit with all the intrinsic and extrinsic synapses. This cell was programmatically extracted from the Somatosensory cortex(SSCx) circuit publish in [Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation)](https://doi.org/10.7554/eLife.99693.3)
+
+We also call such single cells with all its synapses, a **Synaptome**.
+
+A synaptome named **nbS1-O1__202247__cADpyr__L5_TPC_A** is used in this example. For more details:
+
+**[Open the synaptome in Open Brain Institute's Virtual Labs ](https://www.openbraininstitute.org/app/entity/82bae430-aff9-46b1-8ce7-e616bc733ed2)**.
+
+Once you login, you can view all the details, subcircuits and circuit analysis on the detailed circuit page.
+
+Locally, it is present in this folder: 
+`BlueCelluLab/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A`
+
+The cell is stored in SONATA format with all the components including the H5 morphology. We also have the morphology in a H5 container containing a single morphology. Other 
+
+## Key Features
+- **H5 Container Morphologies**: Uses morphologies stored in H5 containers for efficient storage and access
+- **MPI Support**: Configurable parallel execution using MPI
+- **NWB Export**: Optional Neurodata Without Borders format output
+- **Flexible Stimuli**: Multiple current clamp stimulus configurations
+- **Comprehensive Reporting**: Soma voltage reporting with configurable sampling
+
+
+## Core Configuration Files
+- **`run_sonata.sh`** - Bash script to execute the simulation
+- **`run_bluecellulab_simulation.py`** - Python simulation runner script
+- **`simulation_config_container.json`** - Main simulation configuration file
+- **`node_sets.json`** - Node set definitions for the circuit
+- The circuit is defined in the "../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A" folder.
+
+The folder also contains `simulation_config_swc.json`, `simulation_config_asc.json` and `simulation_config_h5.json` files for running the simulation with swc, asc and h5 individual morphology formats respectively. Change the `simulation_config` variable in `run_sonata.sh` to run different simulation configs  to simulate with different morphology formats.
+
+## Prerequisites
+- Use a virtual environment with BlueCelluLab installed
+- MPI installation: mpi4py or openmpi
+
+## Running the Simulation
+**Execute the simulation script:**
+   Update the virtual environment path in the run_sonata.sh script if needed.
+   The script supports parallel execution via MPI. Modify the `num_cores` variable in `run_sonata.sh`:
+   It is set to 1 by default.
+   ```bash
+   ./run_sonata.sh
+   ```
+
+** Or run directly with Python:**
+   ```bash
+   python run_bluecellulab_simulation.py --simulation_config simulation_config_container.json --save-nwb
+   ```
+
+NB: The `num_cores=1` is set the `run_sonata.sh` script on a single core. `num_cores` is the number of cores to use for parallel execution and should be less or equal to the number of cells to be be simualted. 
+
+## Output
+The outputs are stored in the `output_container` directory. The `output_swc`, `output_asc`, `output_h5` directories are used for storing the outputs of the swc, asc and individual h5 morphology file formats respectively. These 
+
+- **Spikes file**: `spikes.h5`
+- **Soma report**: `soma_report.h5`
+- **NWB file**: soma recordings from all neurons of the node set: `simulation_YYYYMMDD_HHMMSS.nwb`
+
+Logs are stored in the `logs` directory: `simulation_YYYYMMDD_HHMMSS.log`
diff --git a/examples/2-sonata-network/run_circuit_mpi/node_sets.json b/examples/2-sonata-network/run_circuit_mpi/node_sets.json
new file mode 100644
index 00000000..619fb1ee
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/node_sets.json
@@ -0,0 +1,817 @@
+{
+  "Mosaic": [
+    "All"
+  ],
+  "All": [
+    "L4_SBC",
+    "L5_TPC:B",
+    "L4_NBC",
+    "L4_DBC",
+    "L23_DBC",
+    "L5_TPC:A",
+    "L3_TPC:A",
+    "L4_BTC",
+    "L6_BTC",
+    "L4_UPC",
+    "L6_LBC",
+    "L6_IPC",
+    "L23_BP",
+    "L4_MC",
+    "L5_DBC",
+    "L6_NBC",
+    "L1_LAC",
+    "L6_CHC",
+    "L23_MC",
+    "L1_HAC",
+    "L6_NGC",
+    "L4_NGC",
+    "L2_IPC",
+    "L6_HPC",
+    "L5_BTC",
+    "L4_CHC",
+    "L5_NGC",
+    "L4_BP",
+    "L2_TPC:A",
+    "L5_SBC",
+    "L5_UPC",
+    "L6_DBC",
+    "L23_CHC",
+    "L5_NBC",
+    "L1_NGC-SA",
+    "L6_BPC",
+    "L6_UPC",
+    "L23_LBC",
+    "L4_SSC",
+    "L5_LBC",
+    "L6_SBC",
+    "L1_NGC-DA",
+    "L6_TPC:C",
+    "L23_SBC",
+    "L5_MC",
+    "L1_DAC",
+    "L2_TPC:B",
+    "L6_TPC:A",
+    "L5_CHC",
+    "L23_NBC",
+    "L3_TPC:C",
+    "L6_BP",
+    "L4_LBC",
+    "L5_BP",
+    "L23_BTC",
+    "L6_MC",
+    "L1_SAC",
+    "L4_TPC",
+    "L5_TPC:C",
+    "L23_NGC"
+  ],
+  "Excitatory": {
+    "synapse_class": "EXC"
+  },
+  "Inhibitory": {
+    "synapse_class": "INH"
+  },
+  "L1_DAC": {
+    "mtype": "L1_DAC"
+  },
+  "L1_HAC": {
+    "mtype": "L1_HAC"
+  },
+  "L1_LAC": {
+    "mtype": "L1_LAC"
+  },
+  "L1_NGC-DA": {
+    "mtype": "L1_NGC-DA"
+  },
+  "L1_NGC-SA": {
+    "mtype": "L1_NGC-SA"
+  },
+  "L1_SAC": {
+    "mtype": "L1_SAC"
+  },
+  "L23_BP": {
+    "mtype": "L23_BP"
+  },
+  "L23_BTC": {
+    "mtype": "L23_BTC"
+  },
+  "L23_CHC": {
+    "mtype": "L23_CHC"
+  },
+  "L23_DBC": {
+    "mtype": "L23_DBC"
+  },
+  "L23_LBC": {
+    "mtype": "L23_LBC"
+  },
+  "L23_MC": {
+    "mtype": "L23_MC"
+  },
+  "L23_NBC": {
+    "mtype": "L23_NBC"
+  },
+  "L23_NGC": {
+    "mtype": "L23_NGC"
+  },
+  "L23_SBC": {
+    "mtype": "L23_SBC"
+  },
+  "L2_IPC": {
+    "mtype": "L2_IPC"
+  },
+  "L2_TPC:A": {
+    "mtype": "L2_TPC:A"
+  },
+  "L2_TPC:B": {
+    "mtype": "L2_TPC:B"
+  },
+  "L3_TPC:A": {
+    "mtype": "L3_TPC:A"
+  },
+  "L3_TPC:C": {
+    "mtype": "L3_TPC:C"
+  },
+  "L4_BP": {
+    "mtype": "L4_BP"
+  },
+  "L4_BTC": {
+    "mtype": "L4_BTC"
+  },
+  "L4_CHC": {
+    "mtype": "L4_CHC"
+  },
+  "L4_DBC": {
+    "mtype": "L4_DBC"
+  },
+  "L4_LBC": {
+    "mtype": "L4_LBC"
+  },
+  "L4_MC": {
+    "mtype": "L4_MC"
+  },
+  "L4_NBC": {
+    "mtype": "L4_NBC"
+  },
+  "L4_NGC": {
+    "mtype": "L4_NGC"
+  },
+  "L4_SBC": {
+    "mtype": "L4_SBC"
+  },
+  "L4_SSC": {
+    "mtype": "L4_SSC"
+  },
+  "L4_TPC": {
+    "mtype": "L4_TPC"
+  },
+  "L4_UPC": {
+    "mtype": "L4_UPC"
+  },
+  "L5_BP": {
+    "mtype": "L5_BP"
+  },
+  "L5_BTC": {
+    "mtype": "L5_BTC"
+  },
+  "L5_CHC": {
+    "mtype": "L5_CHC"
+  },
+  "L5_DBC": {
+    "mtype": "L5_DBC"
+  },
+  "L5_LBC": {
+    "mtype": "L5_LBC"
+  },
+  "L5_MC": {
+    "mtype": "L5_MC"
+  },
+  "L5_NBC": {
+    "mtype": "L5_NBC"
+  },
+  "L5_NGC": {
+    "mtype": "L5_NGC"
+  },
+  "L5_SBC": {
+    "mtype": "L5_SBC"
+  },
+  "L5_TPC:A": {
+    "mtype": "L5_TPC:A"
+  },
+  "L5_TPC:B": {
+    "mtype": "L5_TPC:B"
+  },
+  "L5_TPC:C": {
+    "mtype": "L5_TPC:C"
+  },
+  "L5_UPC": {
+    "mtype": "L5_UPC"
+  },
+  "L6_BP": {
+    "mtype": "L6_BP"
+  },
+  "L6_BPC": {
+    "mtype": "L6_BPC"
+  },
+  "L6_BTC": {
+    "mtype": "L6_BTC"
+  },
+  "L6_CHC": {
+    "mtype": "L6_CHC"
+  },
+  "L6_DBC": {
+    "mtype": "L6_DBC"
+  },
+  "L6_HPC": {
+    "mtype": "L6_HPC"
+  },
+  "L6_IPC": {
+    "mtype": "L6_IPC"
+  },
+  "L6_LBC": {
+    "mtype": "L6_LBC"
+  },
+  "L6_MC": {
+    "mtype": "L6_MC"
+  },
+  "L6_NBC": {
+    "mtype": "L6_NBC"
+  },
+  "L6_NGC": {
+    "mtype": "L6_NGC"
+  },
+  "L6_SBC": {
+    "mtype": "L6_SBC"
+  },
+  "L6_TPC:A": {
+    "mtype": "L6_TPC:A"
+  },
+  "L6_TPC:C": {
+    "mtype": "L6_TPC:C"
+  },
+  "L6_UPC": {
+    "mtype": "L6_UPC"
+  },
+  "bAC": {
+    "etype": "bAC"
+  },
+  "bIR": {
+    "etype": "bIR"
+  },
+  "bNAC": {
+    "etype": "bNAC"
+  },
+  "bSTUT": {
+    "etype": "bSTUT"
+  },
+  "cACint": {
+    "etype": "cACint"
+  },
+  "cADpyr": {
+    "etype": "cADpyr"
+  },
+  "cIR": {
+    "etype": "cIR"
+  },
+  "cNAC": {
+    "etype": "cNAC"
+  },
+  "cSTUT": {
+    "etype": "cSTUT"
+  },
+  "dNAC": {
+    "etype": "dNAC"
+  },
+  "dSTUT": {
+    "etype": "dSTUT"
+  },
+  "Layer1": {
+    "layer": "1"
+  },
+  "Layer2": {
+    "layer": "2"
+  },
+  "Layer3": {
+    "layer": "3"
+  },
+  "Layer4": {
+    "layer": "4"
+  },
+  "Layer5": {
+    "layer": "5"
+  },
+  "Layer6": {
+    "layer": "6"
+  },
+  "Layer2Excitatory": [
+    "L2_TPC:A",
+    "L2_TPC:B",
+    "L2_IPC"
+  ],
+  "Layer3Excitatory": [
+    "L3_TPC:C",
+    "L3_TPC:A"
+  ],
+  "Layer23Excitatory": [
+    "Layer3Excitatory",
+    "Layer2Excitatory"
+  ],
+  "Layer4Excitatory": [
+    "L4_UPC",
+    "L4_TPC",
+    "L4_SSC"
+  ],
+  "Layer5Excitatory": [
+    "L5_UPC",
+    "L5_TPC:A",
+    "L5_TPC:B",
+    "L5_TPC:C"
+  ],
+  "Layer6Excitatory": [
+    "L6_BPC",
+    "L6_HPC",
+    "L6_UPC",
+    "L6_TPC:C",
+    "L6_IPC",
+    "L6_TPC:A"
+  ],
+  "Layer1Inhibitory": [
+    "L1_HAC",
+    "L1_NGC-SA",
+    "L1_NGC-DA",
+    "L1_DAC",
+    "L1_SAC",
+    "L1_LAC"
+  ],
+  "Layer23Inhibitory": [
+    "L23_NGC",
+    "L23_DBC",
+    "L23_LBC",
+    "L23_BTC",
+    "L23_SBC",
+    "L23_BP",
+    "L23_MC",
+    "L23_NBC",
+    "L23_CHC"
+  ],
+  "Layer4Inhibitory": [
+    "L4_SBC",
+    "L4_NBC",
+    "L4_DBC",
+    "L4_NGC",
+    "L4_LBC",
+    "L4_BTC",
+    "L4_CHC",
+    "L4_BP",
+    "L4_MC"
+  ],
+  "Layer5Inhibitory": [
+    "L5_NBC",
+    "L5_BTC",
+    "L5_BP",
+    "L5_LBC",
+    "L5_NGC",
+    "L5_MC",
+    "L5_DBC",
+    "L5_SBC",
+    "L5_CHC"
+  ],
+  "Layer6Inhibitory": [
+    "L6_NGC",
+    "L6_BP",
+    "L6_BTC",
+    "L6_SBC",
+    "L6_LBC",
+    "L6_MC",
+    "L6_NBC",
+    "L6_DBC",
+    "L6_CHC"
+  ],
+  "hex0": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ]
+  },
+  "hex1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex2": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex3": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ]
+  },
+  "hex6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex_O1": [
+    "hex6",
+    "hex0",
+    "hex4",
+    "hex2",
+    "hex3",
+    "hex5",
+    "hex1"
+  ],
+  "hex_O1PV": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      4
+    ]
+  },
+  "hex_O1Sst": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex_O15HT3aR": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "proj_Thalamocortical_VPM_Source": {
+    "population": "VPM"
+  },
+  "proj_Thalamocortical_POM_Source": {
+    "population": "POm"
+  },
+  "nbS1-HEX0": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ]
+  },
+  "nbS1-HEX1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX2": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX3": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ]
+  },
+  "nbS1-HEX6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX0-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX1-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX2-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX3-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX4-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX5-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX6-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX0-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX1-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX2-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX3-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX4-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX5-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX6-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX0-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX1-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX2-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX3-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX4-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX5-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX6-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX0-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX1-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX2-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX3-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX4-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX5-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX6-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX0-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0,
+      1,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9
+    ],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX1-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX2-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX3-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX4-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX5-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      2
+    ],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX6-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "Default_All_Biophysical_Neurons": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ]
+  }
+}
+
diff --git a/examples/2-sonata-network/run_circuit_mpi/run_bluecellulab_simulation.py b/examples/2-sonata-network/run_circuit_mpi/run_bluecellulab_simulation.py
new file mode 100644
index 00000000..9d3d3e5f
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/run_bluecellulab_simulation.py
@@ -0,0 +1,490 @@
+"""
+Usage:
+    python run_bluecellulab_simulation.py --simulation_config <simulation_config> [--save-nwb]
+"""
+
+import argparse
+from collections import defaultdict
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Dict, Any, Union
+import numpy as np
+from bluecellulab import CircuitSimulation
+from bluecellulab.reports.manager import ReportManager
+from neuron import h
+from pynwb import NWBFile, NWBHDF5IO
+from pynwb.icephys import CurrentClampSeries, IntracellularElectrode
+from datetime import datetime, timezone
+import uuid
+import matplotlib
+matplotlib.use('Agg') # non-interactive backend for matplotlib to avoid display issues
+import matplotlib.pyplot as plt
+
+# Create logs directory if it doesn't exist
+log_dir = Path('logs')
+log_dir.mkdir(exist_ok=True)
+
+# Set up log file with timestamp
+timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+log_file = log_dir / f'simulation_{timestamp}.log'
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler(log_file)
+    ]
+)
+logger = logging.getLogger(__name__)
+logger.info(f"Logging to {log_file}")
+
+# ---- merge helpers ---------------------------------------------
+def _merge_dicts(list_of_dicts):
+    merged: Dict[Any, Any] = {}
+    for d in list_of_dicts:
+        merged.update(d)
+    return merged
+
+def _merge_spikes(list_of_pop_dicts):
+    out: Dict[str, Dict[int, list]] = defaultdict(dict)
+    for pop_dict in list_of_pop_dicts:
+        for pop, gid_map in pop_dict.items():
+            out[pop].update(gid_map)
+    return out
+
+def get_instantiate_gids_params(simulation_config_data: Dict[str, Any]) -> Dict[str, Any]:
+    """Determine instantiate_gids parameters from simulation config.
+
+    This function gives parameters for sim.instantiate_gids() based on the
+    simulation config. See the package BlueCellulab/bluecellulab/circuit_simulation.py
+    for more details.
+
+    Args:
+        simulation_config_data: Loaded simulation configuration
+    Returns:
+        Dictionary of parameters for instantiate_gids
+    """
+    params = {
+        # Core parameters - these are the main ones we need to set
+        'add_stimuli': False,
+        'add_synapses': False,
+        'add_minis': False,
+        'add_replay': False,
+        'add_projections': False,
+        'interconnect_cells': True,
+        # These will be handled automatically by add_stimuli=True
+        'add_noise_stimuli': False,
+        'add_hyperpolarizing_stimuli': False,
+        'add_relativelinear_stimuli': False,
+        'add_pulse_stimuli': False,
+        'add_shotnoise_stimuli': False,
+        'add_ornstein_uhlenbeck_stimuli': False,
+        'add_sinusoidal_stimuli': False,
+        'add_linear_stimuli': False,
+    }
+
+    # Check for any inputs in the config
+    if 'inputs' in simulation_config_data and simulation_config_data['inputs']:
+        params['add_stimuli'] = True
+
+        # Log any unsupported input types
+        supported_types = {
+            'noise', 'hyperpolarizing', 'relativelinear', 'pulse',
+            'sinusoidal', 'linear', 'shotnoise', 'ornstein_uhlenbeck'
+        }
+
+        for input_def in simulation_config_data['inputs'].values():
+            module = input_def.get('module', '').lower()
+            if module not in supported_types:
+                logger.warning(f"Input type '{module}' may not be fully supported by instantiate_gids")
+
+    # Check for synapses and minis in conditions
+    if 'conditions' in simulation_config_data:
+        conditions = simulation_config_data['conditions']
+        if 'mechanisms' in conditions and conditions['mechanisms']:
+            params['add_synapses'] = True
+            # Check if any mechanism has minis enabled
+            for mech in conditions['mechanisms'].values():
+                if mech.get('minis_single_vesicle', False):
+                    params['add_minis'] = True
+                    break
+
+    # # Check for spike replay in inputs
+    # if 'inputs' in simulation_config_data:
+    #     for input_def in simulation_config_data['inputs'].values():
+    #         if input_def.get('module') == 'synapse_replay':
+    #             params['add_replay'] = True
+    #             break
+
+    # Enable projections by default if synapses are enabled
+    # params['add_projections'] = params['add_synapses']
+    # params['add_minis'] = True
+
+    return params
+
+def plot_voltage_traces(results: Dict[str, Any], output_path: Union[str, Path], max_cols: int = 3):
+    """Plot voltage traces for all cells in a grid of subplots and save to file.
+
+    Args:
+        results: Dictionary containing simulation results for each cell
+        output_path: Path where to save the plot (should include .png extension)
+        max_cols: Maximum number of columns in the subplot grid
+    """
+    n_cells = len(results)
+    if n_cells == 0:
+        logger.warning("No voltage traces to plot")
+        return
+
+    # Calculate grid size
+    n_cols = min(max_cols, n_cells)
+    n_rows = (n_cells + n_cols - 1) // n_cols
+
+    # Create figure with subplots
+    fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 3 * n_rows),
+                            squeeze=False, constrained_layout=True)
+
+    # Flatten axes for easier iteration
+    axes = axes.ravel()
+
+    # Plot each cell's voltage trace in its own subplot
+    for idx, (cell_id, trace) in enumerate(results.items()):
+        ax = axes[idx]
+        time_ms = np.array(trace["time"])
+        voltage_mv = np.array(trace["voltage"])
+
+        ax.plot(time_ms, voltage_mv, linewidth=1)
+        ax.set_title(f"Cell {cell_id}", fontsize=10)
+        ax.grid(True, alpha=0.3)
+
+        # Only label bottom row x-axes
+        if idx >= (n_rows - 1) * n_cols:
+            ax.set_xlabel("Time (ms)", fontsize=8)
+
+        # Only label leftmost column y-axes
+        if idx % n_cols == 0:
+            ax.set_ylabel("mV", fontsize=8)
+
+    # Turn off unused subplots
+    for idx in range(n_cells, len(axes)):
+        axes[idx].axis('off')
+
+    # Add a main title
+    fig.suptitle(f"Voltage Traces for {n_cells} Cells", fontsize=12)
+
+    # Save the figure
+    plt.savefig(output_path, dpi=150, bbox_inches='tight')
+    plt.close(fig)
+    logger.info(f"Saved voltage traces plot to {output_path}")
+def save_results_to_nwb(results: Dict[str, Any], output_path: Union[str, Path]):
+    """Save simulation results to NWB format"""
+    try:
+        nwbfile = NWBFile(
+            session_description="Small Microcircuit Simulation results",
+            identifier=str(uuid.uuid4()),
+            session_start_time=datetime.now(timezone.utc),
+            experimenter="BlueCelluLab",
+            lab="BlueCelluLab",
+            institution="OBI",
+            experiment_description="Simulation results",
+            session_id="small_microcircuit_simulation"
+        )
+
+        # Add device and electrode
+        device = nwbfile.create_device(
+            name="SimulatedElectrode",
+            description="Virtual electrode for simulation recording"
+        )
+
+        # Add voltage traces
+        for cell_id, trace in results.items():
+            # Create electrode for this cell
+            electrode = IntracellularElectrode(
+                name=f"electrode_{cell_id}",
+                description=f"Simulated electrode for {cell_id}",
+                device=device,
+                location="soma",
+                filtering="none"
+            )
+            nwbfile.add_icephys_electrode(electrode)
+
+            # Convert time from ms to seconds for NWB
+            time_data = np.array(trace["time"], dtype=float) / 1000.0
+            voltage_data = np.array(trace["voltage"], dtype=float) / 1000.0  # Convert mV to V
+
+            # Create current clamp series with timestamps
+            ics = CurrentClampSeries(
+                name=f"voltage_{cell_id}",
+                data=voltage_data,
+                electrode=electrode,
+                timestamps=time_data,
+                gain=1.0,
+                unit="volts",
+                description=f"Voltage trace for {cell_id}",
+            )
+            nwbfile.add_acquisition(ics)
+
+        # Save to file
+        output_path = Path(output_path)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Save to file
+        with NWBHDF5IO(str(output_path), 'w') as io:
+            io.write(nwbfile)
+
+        logger.info(f"Successfully saved results to {output_path}")
+
+    except Exception as e:
+        logger.error(f"Error saving results to NWB: {str(e)}")
+        raise
+
+
+def run_bluecellulab(
+    simulation_config: Union[str, Path],
+    save_nwb: bool = False,
+) -> None:
+    """Run a simulation using BlueCelluLab backend.
+
+    Args:
+        simulation_config: Path to the simulation configuration file
+        save_nwb: Whether to save results in NWB format.
+    """
+    logger = logging.getLogger(__name__)
+
+    # Get MPI info using NEURON's ParallelContext
+    h.nrnmpi_init()
+    pc = h.ParallelContext()
+    rank = int(pc.id())
+    size = int(pc.nhost())
+
+    if rank == 0:
+        logger.info("Initializing BlueCelluLab simulation")
+
+    # Load configuration using json
+    with open(simulation_config) as f:
+        simulation_config_data = json.load(f)
+
+    # Get simulation parameters from config
+    t_stop = simulation_config_data["run"]["tstop"]
+    dt = simulation_config_data["run"]["dt"]
+
+    try:
+        # Get the directory of the simulation config
+        sim_config_base_dir = Path(simulation_config).parent
+        logger.info(f"sim_config_base_dir: {sim_config_base_dir}")
+
+        # Get manifest path
+        OUTPUT_DIR = simulation_config_data.get("manifest", {}).get("$OUTPUT_DIR", "./")
+        logger.info(f"OUTPUT_DIR: {OUTPUT_DIR}")
+
+        # Get the node_set
+        node_set_name = simulation_config_data.get("node_set", "All")
+
+        node_sets_file = sim_config_base_dir / simulation_config_data["node_sets_file"]
+        logger.info(f"node_sets_file: {node_sets_file}")
+
+        with open(node_sets_file) as f:
+            node_set_data = json.load(f)
+
+        # Get population and node IDs
+        if node_set_name not in node_set_data:
+            raise KeyError(f"Node set '{node_set_name}' not found in node sets file")
+
+        population = node_set_data[node_set_name]["population"]
+        all_node_ids = node_set_data[node_set_name]["node_id"]
+        logger.info(f"Population: {population}")
+        logger.info(f"All node IDs: {all_node_ids}")
+
+        # Distribute nodes across ranks
+        num_nodes = len(all_node_ids)
+        nodes_per_rank = num_nodes // size
+        remainder = num_nodes % size
+        logger.info(f"Total nodes: {num_nodes}, Nodes per rank: {nodes_per_rank}, Remainder: {remainder}")
+
+        # Calculate start and end indices for this rank
+        start_idx = rank * nodes_per_rank + min(rank, remainder)
+        if rank < remainder:
+            nodes_per_rank += 1
+        end_idx = start_idx + nodes_per_rank
+        logger.info(f"Rank {rank}: start_idx={start_idx}, end_idx={end_idx}")
+
+        # Get node IDs for this rank
+        rank_node_ids = all_node_ids[start_idx:end_idx]
+        logger.info(f"Rank {rank} node IDs: {rank_node_ids}")
+        # create cell_ids_for_this_rank
+        cell_ids_for_this_rank = [(population, i) for i in rank_node_ids]
+        logger.info(f"Rank {rank}: Handling {len(cell_ids_for_this_rank)} cells")
+
+        if not cell_ids_for_this_rank:
+            logger.warning(f"Rank {rank}: No cells to process")
+
+        if rank == 0:
+            logger.info(f"Running BlueCelluLab simulation with {size} MPI processes")
+            logger.info(f"Total cells: {num_nodes}, Cells per rank: ~{num_nodes//size}")
+            logger.info(f"Starting simulation: t_stop={t_stop}ms, dt={dt}ms")
+
+        logger.info(f"Rank {rank}: Processing {len(rank_node_ids)} cells (IDs: {rank_node_ids[0]}...{rank_node_ids[-1] if rank_node_ids else 'None'})")
+
+        # Create simulation
+        sim = CircuitSimulation(simulation_config)
+
+        # Get instantiate_gids arguments from config
+        instantiate_params = get_instantiate_gids_params(simulation_config_data)
+
+        if rank == 0:
+            logger.info("Instantiate arguments from config:")
+            for param, value in instantiate_params.items():
+                if value:  # Only log parameters that are True
+                    logger.info(f"  {param}: {value}")
+
+    except Exception as e:
+        logger.error(f"Error during initialization: {str(e)}")
+        raise
+
+    try:
+        logger.info(f"Rank {rank}: Instantiating cells...")
+        # Instantiate cells on this rank with arguments from config
+        sim.instantiate_gids(cell_ids_for_this_rank, **instantiate_params)
+
+        # Run simulation
+        logger.info(f"Rank {rank}: Running simulation...")
+        sim.run(t_stop, cvode=False)
+
+        # Get time trace once for all cells
+        time_ms = sim.get_time_trace()
+        if time_ms is None:
+            logger.error(f"Rank {rank}: Time trace is None, cannot proceed with saving.")
+            return
+
+        time_s = time_ms / 1000.0  # Convert ms to seconds
+
+        # Get voltage traces and spikes for each cell on this rank
+        results_traces: Dict[str, Any] = {}
+        results_spikes: Dict[str, Dict[int, list]] = defaultdict(dict)  # pop → gid → spikes
+        for cell_id in cell_ids_for_this_rank:
+            gid_key = f"{cell_id[0]}_{cell_id[1]}"
+
+            # voltage trace ----------------------------------------------------
+            voltage = sim.get_voltage_trace(cell_id)
+            if voltage is not None:
+                results_traces[gid_key] = {
+                    "time": time_s.tolist(),
+                    "voltage": voltage.tolist(),
+                    "unit": "mV",
+                }
+
+            # spikes -----------------------------------------------------------
+            pop = cell_id[0]
+            gid = cell_id[1]
+            results_spikes[pop][gid] = []
+            try:
+                cell_obj = sim.cells[cell_id]
+                spikes = cell_obj.get_recorded_spikes(
+                    location=sim.spike_location, threshold=sim.spike_threshold
+                )
+                if spikes is not None and len(spikes):
+                    pop = cell_id[0]
+                    results_spikes[pop][cell_id[1]] = list(spikes)
+            except Exception:
+                pass  # silently skip cells without spike recordings
+
+
+        # Gather all results to rank 0
+        logger.info(f"Rank {rank}: Gathering results...")
+        try:
+            # gathered_results = pc.py_gather(results, 0)
+            gathered_traces = pc.py_gather(results_traces, 0)
+            gathered_spikes = pc.py_gather(results_spikes, 0)
+        except Exception as e:
+            logger.error(f"Rank {rank}: Error gathering results: {str(e)}")
+
+        if rank == 0:
+
+            # ---- SONATA reports --------------------------------------------
+            all_traces = _merge_dicts(gathered_traces)
+            all_spikes = _merge_spikes(gathered_spikes)
+
+            report_mgr = ReportManager(sim.circuit_access.config, sim.dt)
+            report_mgr.write_all(
+                cells_or_traces   = all_traces,
+                spikes_by_pop     = all_spikes
+            )
+            # ----------------------------------------------------------------
+
+            if save_nwb:
+                # Get output directory from config, handling all cases
+                base_dir = Path(simulation_config).parent
+                output_dir = None
+
+                # if output_dir is explicitly specified in config
+                if "output" in simulation_config_data and "output_dir" in simulation_config_data["output"]:
+                    output_dir_str = simulation_config_data["output"]["output_dir"]
+                    # Handle $OUTPUT_DIR variable if present
+                    if output_dir_str.startswith("$OUTPUT_DIR"):
+                        if "manifest" in simulation_config_data and "$OUTPUT_DIR" in simulation_config_data["manifest"]:
+                            output_dir = Path(simulation_config_data["manifest"]["$OUTPUT_DIR"]) / output_dir_str.replace("$OUTPUT_DIR/", "")
+                    else:
+                        output_dir = Path(output_dir_str)
+
+                    # Make path absolute if it's relative
+                    if not output_dir.is_absolute():
+                        output_dir = base_dir / output_dir
+
+                # if output_dir not specified or invalid
+                if output_dir is None:
+                    output_dir = base_dir / "output"
+
+                # Ensure output directory exists
+                output_dir.mkdir(parents=True, exist_ok=True)
+
+                # Save NWB file directly in the output directory
+                output_path = output_dir / "results.nwb"
+                logger.info(f"Saving simulation results to: {output_path}")
+                save_results_to_nwb(all_traces, output_path)
+
+                # Save voltage traces plot
+                plot_path = output_dir / "voltage_traces.png"
+                plot_voltage_traces(all_traces, plot_path)
+                logger.info(f"Successfully saved voltage traces plot to {plot_path}")
+
+    except Exception as e:
+        logger.error(f"Rank {rank} failed: {str(e)}", exc_info=True)
+        raise
+    finally:
+        try:
+            # Ensure proper cleanup
+            logger.info(f"Rank {rank}: Cleaning up...")
+            pc.barrier()
+            if rank == 0:
+                logger.info("All ranks completed. Simulation finished.")
+        except Exception as e:
+            logger.error(f"Error during cleanup in rank {rank}: {str(e)}")
+
+def main():
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description='Run a BlueCelluLab Circuit Simulation.')
+    parser.add_argument('--simulation_config', '--config', type=str, required=True,
+                      help='Path to the simulation configuration file')
+    parser.add_argument('--save-nwb', action='store_true',
+                      help='Save results in NWB format')
+
+    args = parser.parse_args()
+
+    # Validate simulation config exists
+    config_path = Path(args.simulation_config)
+    if not config_path.exists():
+        logger.error(f"Configuration file not found: {config_path}")
+        return 1
+
+    # Run the simulation
+    run_bluecellulab(
+        simulation_config=args.simulation_config,
+        save_nwb=args.save_nwb,
+    )
+    return 0
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/2-sonata-network/run_circuit_mpi/run_sonata.sh b/examples/2-sonata-network/run_circuit_mpi/run_sonata.sh
new file mode 100755
index 00000000..a62ca316
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/run_sonata.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+## This example runs the SONATA circuit with morphologies in an h5 container
+## The circuit is defined in the container_nbS1-O1__202247__cADpyr__L5_TPC_A folder
+## The simulation config and nodes file are in the container_configs_nbS1-O1__202247__cADpyr__L5_TPC_A folder
+## The scipt calls the run_bluecellulab_simulation.py script to run the simulation 
+## with the specified simulation config and uses num_cores cores to run the simulation
+## in parallel using mpiexec.
+
+source ../../../.venv/bin/activate
+
+# Remove old compiled mod files
+rm -r arm64/ # for mac-based systems
+rm -r x86_64/ # for linux-based systems
+ 
+circuit_folder="../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A"
+simulation_config="simulation_config_container.json"
+
+## Change the simulation_config to run different simulation configs 
+## to test with different morphology formats
+
+# simulation_config="simulation_config_h5.json"
+# simulation_config="simulation_config_swc.json"
+# simulation_config="simulation_config_asc.json"
+
+# Compile mod files
+# flag DISABLE_REPORTINGLIB to skip SonataReportHelper.mod and SonataReport.mod from compilation.
+nrnivmodl -incflags "-DDISABLE_REPORTINGLIB" $circuit_folder/mod  # Replace with the actual path to your mod files
+
+echo "Running circuit simulation"
+# NB: num_cores should be less than or equal to number of cells to be simulated
+num_cores=1
+mpiexec -n $num_cores python run_bluecellulab_simulation.py --simulation_config $simulation_config --save-nwb
\ No newline at end of file
diff --git a/examples/2-sonata-network/run_circuit_mpi/simulation_config_asc.json b/examples/2-sonata-network/run_circuit_mpi/simulation_config_asc.json
new file mode 100644
index 00000000..65a45355
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/simulation_config_asc.json
@@ -0,0 +1,65 @@
+{
+  "version": 2.4,
+  "target_simulator": "NEURON",
+  "run": {
+    "dt": 0.025,
+    "random_seed": 1,
+    "tstop": 2000
+  },
+  "conditions": {
+    "extracellular_calcium": 1.1,
+    "v_init": -80,
+    "spike_location": "soma",
+    "mechanisms": {
+      "ProbAMPANMDA_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      },
+      "ProbGABAAB_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      }
+    }
+  },
+  "output": {
+    "output_dir": "output_asc",
+    "spikes_file": "spikes.h5"
+  },
+  "network": "../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_asc.json",
+  "node_set": "Default_All_Biophysical_Neurons",
+  "inputs": {
+    "Stimulus_0_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "hyperpolarizing",
+      "input_type": "current_clamp",
+      "represents_physical_electrode": false
+    },
+    "Stimulus_1_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "relative_linear",
+      "input_type": "current_clamp",
+      "percent_start": 150,
+      "represents_physical_electrode": false
+    }
+  },
+  "reports": {
+    "soma_report": {
+      "cells": "Default_All_Biophysical_Neurons",
+      "sections": "soma",
+      "type": "compartment",
+      "variable_name": "v",
+      "unit": "mV",
+      "dt": 0.1,
+      "start_time" : 0,
+      "end_time" : 2000,
+      "file_name": "soma_report",
+      "enabled" : true
+    }
+  },
+  "node_sets_file": "node_sets.json"
+}
+
diff --git a/examples/2-sonata-network/run_circuit_mpi/simulation_config_container.json b/examples/2-sonata-network/run_circuit_mpi/simulation_config_container.json
new file mode 100644
index 00000000..f85de05e
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/simulation_config_container.json
@@ -0,0 +1,65 @@
+{
+  "version": 2.4,
+  "target_simulator": "NEURON",
+  "run": {
+    "dt": 0.025,
+    "random_seed": 1,
+    "tstop": 2000
+  },
+  "conditions": {
+    "extracellular_calcium": 1.1,
+    "v_init": -80,
+    "spike_location": "soma",
+    "mechanisms": {
+      "ProbAMPANMDA_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      },
+      "ProbGABAAB_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      }
+    }
+  },
+  "output": {
+    "output_dir": "output_container",
+    "spikes_file": "spikes.h5"
+  },
+  "network": "../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config.json",
+  "node_set": "Default_All_Biophysical_Neurons",
+  "inputs": {
+    "Stimulus_0_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "hyperpolarizing",
+      "input_type": "current_clamp",
+      "represents_physical_electrode": false
+    },
+    "Stimulus_1_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "relative_linear",
+      "input_type": "current_clamp",
+      "percent_start": 150,
+      "represents_physical_electrode": false
+    }
+  },
+  "reports": {
+    "soma_report": {
+      "cells": "Default_All_Biophysical_Neurons",
+      "sections": "soma",
+      "type": "compartment",
+      "variable_name": "v",
+      "unit": "mV",
+      "dt": 0.1,
+      "start_time" : 0,
+      "end_time" : 2000,
+      "file_name": "soma_report",
+      "enabled" : true
+    }
+  },
+  "node_sets_file": "node_sets.json"
+}
+
diff --git a/examples/2-sonata-network/run_circuit_mpi/simulation_config_h5.json b/examples/2-sonata-network/run_circuit_mpi/simulation_config_h5.json
new file mode 100644
index 00000000..6e02710c
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/simulation_config_h5.json
@@ -0,0 +1,65 @@
+{
+  "version": 2.4,
+  "target_simulator": "NEURON",
+  "run": {
+    "dt": 0.025,
+    "random_seed": 1,
+    "tstop": 2000
+  },
+  "conditions": {
+    "extracellular_calcium": 1.1,
+    "v_init": -80,
+    "spike_location": "soma",
+    "mechanisms": {
+      "ProbAMPANMDA_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      },
+      "ProbGABAAB_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      }
+    }
+  },
+  "output": {
+    "output_dir": "output_h5",
+    "spikes_file": "spikes.h5"
+  },
+  "network": "../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_h5.json",
+  "node_set": "Default_All_Biophysical_Neurons",
+  "inputs": {
+    "Stimulus_0_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "hyperpolarizing",
+      "input_type": "current_clamp",
+      "represents_physical_electrode": false
+    },
+    "Stimulus_1_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "relative_linear",
+      "input_type": "current_clamp",
+      "percent_start": 150,
+      "represents_physical_electrode": false
+    }
+  },
+  "reports": {
+    "soma_report": {
+      "cells": "Default_All_Biophysical_Neurons",
+      "sections": "soma",
+      "type": "compartment",
+      "variable_name": "v",
+      "unit": "mV",
+      "dt": 0.1,
+      "start_time" : 0,
+      "end_time" : 2000,
+      "file_name": "soma_report",
+      "enabled" : true
+    }
+  },
+  "node_sets_file": "node_sets.json"
+}
+
diff --git a/examples/2-sonata-network/run_circuit_mpi/simulation_config_swc.json b/examples/2-sonata-network/run_circuit_mpi/simulation_config_swc.json
new file mode 100644
index 00000000..5019d123
--- /dev/null
+++ b/examples/2-sonata-network/run_circuit_mpi/simulation_config_swc.json
@@ -0,0 +1,65 @@
+{
+  "version": 2.4,
+  "target_simulator": "NEURON",
+  "run": {
+    "dt": 0.025,
+    "random_seed": 1,
+    "tstop": 2000
+  },
+  "conditions": {
+    "extracellular_calcium": 1.1,
+    "v_init": -80,
+    "spike_location": "soma",
+    "mechanisms": {
+      "ProbAMPANMDA_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      },
+      "ProbGABAAB_EMS": {
+        "init_depleted": true,
+        "minis_single_vesicle": true
+      }
+    }
+  },
+  "output": {
+    "output_dir": "output_swc",
+    "spikes_file": "spikes.h5"
+  },
+  "network": "../../../tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_swc.json",
+  "node_set": "Default_All_Biophysical_Neurons",
+  "inputs": {
+    "Stimulus_0_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "hyperpolarizing",
+      "input_type": "current_clamp",
+      "represents_physical_electrode": false
+    },
+    "Stimulus_1_0": {
+      "delay": 0,
+      "duration": 2000,
+      "node_set": "Default_All_Biophysical_Neurons",
+      "module": "relative_linear",
+      "input_type": "current_clamp",
+      "percent_start": 150,
+      "represents_physical_electrode": false
+    }
+  },
+  "reports": {
+    "soma_report": {
+      "cells": "Default_All_Biophysical_Neurons",
+      "sections": "soma",
+      "type": "compartment",
+      "variable_name": "v",
+      "unit": "mV",
+      "dt": 0.1,
+      "start_time" : 0,
+      "end_time" : 2000,
+      "file_name": "soma_report",
+      "enabled" : true
+    }
+  },
+  "node_sets_file": "node_sets.json"
+}
+
diff --git a/pyproject.toml b/pyproject.toml
index 75a4c699..721c8d27 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,7 @@ dependencies = [
     "efel>=5.7.19",
     "neo>=0.14.0",
     "quantities>=0.16.2",
+    "morphio>=3.0.0",
 ]
 requires-python = ">=3.10"
 
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm/nodes.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm/nodes.h5
new file mode 100644
index 00000000..c2cece8a
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm/nodes.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm__S1nonbarrel_neurons__chemical/edges.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm__S1nonbarrel_neurons__chemical/edges.h5
new file mode 100644
index 00000000..704e1b9e
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/POm__S1nonbarrel_neurons__chemical/edges.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/S1nonbarrel_neurons/nodes.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/S1nonbarrel_neurons/nodes.h5
new file mode 100644
index 00000000..46fd6232
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/S1nonbarrel_neurons/nodes.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM/nodes.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM/nodes.h5
new file mode 100644
index 00000000..c3ea69b4
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM/nodes.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM__S1nonbarrel_neurons__chemical/edges.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM__S1nonbarrel_neurons__chemical/edges.h5
new file mode 100644
index 00000000..f562c2cb
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/VPM__S1nonbarrel_neurons__chemical/edges.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config.json
new file mode 100644
index 00000000..c41cecff
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config.json
@@ -0,0 +1,85 @@
+{
+    "components": {
+        "biophysical_neuron_models_dir": "",
+        "mechanisms_dir": "",
+        "morphologies_dir": "",
+        "point_neuron_models_dir": "",
+        "provenance": {
+            "id_mapping": "id_mapping.json"
+        },
+        "synaptic_models_dir": "",
+        "templates_dir": ""
+    },
+    "networks": {
+        "edges": [
+            {
+                "edges_file": "$BASE_DIR/POm__S1nonbarrel_neurons__chemical/edges.h5",
+                "populations": {
+                    "POm__S1nonbarrel_neurons__chemical": {
+                        "type": "chemical"
+                    }
+                }
+            },
+            {
+                "edges_file": "$BASE_DIR/VPM__S1nonbarrel_neurons__chemical/edges.h5",
+                "populations": {
+                    "VPM__S1nonbarrel_neurons__chemical": {
+                        "type": "chemical"
+                    }
+                }
+            },
+            {
+                "edges_file": "$BASE_DIR/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5",
+                "populations": {
+                    "external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical": {
+                        "type": "chemical"
+                    }
+                }
+            }
+        ],
+        "nodes": [
+            {
+                "nodes_file": "$BASE_DIR/S1nonbarrel_neurons/nodes.h5",
+                "populations": {
+                    "S1nonbarrel_neurons": {
+                        "alternate_morphologies": {
+                            "h5v1": "$BASE_DIR/morphologies/merged-morphologies.h5"
+                        },
+                        "biophysical_neuron_models_dir": "$BASE_DIR/emodels_hoc",
+                        "morphologies_dir": "",
+                        "type": "biophysical"
+                    }
+                }
+            },
+            {
+                "nodes_file": "$BASE_DIR/POm/nodes.h5",
+                "populations": {
+                    "POm": {
+                        "type": "virtual"
+                    }
+                }
+            },
+            {
+                "nodes_file": "$BASE_DIR/VPM/nodes.h5",
+                "populations": {
+                    "VPM": {
+                        "type": "virtual"
+                    }
+                }
+            },
+            {
+                "nodes_file": "$BASE_DIR/external_S1nonbarrel_neurons/nodes.h5",
+                "populations": {
+                    "external_S1nonbarrel_neurons": {
+                        "type": "virtual"
+                    }
+                }
+            }
+        ]
+    },
+    "node_sets_file": "$BASE_DIR/node_sets.json",
+    "version": 2.3,
+    "manifest": {
+        "$BASE_DIR": "./"
+    }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_asc.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_asc.json
new file mode 100644
index 00000000..e1970750
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_asc.json
@@ -0,0 +1,83 @@
+{
+  "components": {
+    "biophysical_neuron_models_dir": "",
+    "mechanisms_dir": "",
+    "morphologies_dir": "",
+    "point_neuron_models_dir": "",
+    "provenance": {
+      "id_mapping": "id_mapping.json"
+    },
+    "synaptic_models_dir": "",
+    "templates_dir": ""
+  },
+  "networks": {
+    "edges": [
+      {
+        "edges_file": "$BASE_DIR/POm__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "POm__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/VPM__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "VPM__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      }
+    ],
+    "nodes": [
+      {
+        "nodes_file": "$BASE_DIR/S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "S1nonbarrel_neurons": {
+            "alternate_morphologies": {"neurolucida-asc": "$BASE_DIR/morphologies/asc"},
+            "biophysical_neuron_models_dir": "$BASE_DIR/emodels_hoc",
+            "morphologies_dir": "",
+            "type": "biophysical"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/POm/nodes.h5",
+        "populations": {
+          "POm": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/VPM/nodes.h5",
+        "populations": {
+          "VPM": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/external_S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons": {
+            "type": "virtual"
+          }
+        }
+      }
+    ]
+  },
+  "node_sets_file": "$BASE_DIR/node_sets.json",
+  "version": 2.3,
+  "manifest": {
+    "$BASE_DIR": "./"
+  }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_h5.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_h5.json
new file mode 100644
index 00000000..fe3a2b89
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_h5.json
@@ -0,0 +1,83 @@
+{
+  "components": {
+    "biophysical_neuron_models_dir": "",
+    "mechanisms_dir": "",
+    "morphologies_dir": "",
+    "point_neuron_models_dir": "",
+    "provenance": {
+      "id_mapping": "id_mapping.json"
+    },
+    "synaptic_models_dir": "",
+    "templates_dir": ""
+  },
+  "networks": {
+    "edges": [
+      {
+        "edges_file": "$BASE_DIR/POm__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "POm__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/VPM__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "VPM__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      }
+    ],
+    "nodes": [
+      {
+        "nodes_file": "$BASE_DIR/S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "S1nonbarrel_neurons": {
+            "alternate_morphologies": {"h5v1": "$BASE_DIR/morphologies/h5"},
+            "biophysical_neuron_models_dir": "$BASE_DIR/emodels_hoc",
+            "morphologies_dir": "",
+            "type": "biophysical"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/POm/nodes.h5",
+        "populations": {
+          "POm": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/VPM/nodes.h5",
+        "populations": {
+          "VPM": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/external_S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons": {
+            "type": "virtual"
+          }
+        }
+      }
+    ]
+  },
+  "node_sets_file": "$BASE_DIR/node_sets.json",
+  "version": 2.3,
+  "manifest": {
+    "$BASE_DIR": "./"
+  }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_swc.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_swc.json
new file mode 100644
index 00000000..1f185bd7
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/circuit_config_swc.json
@@ -0,0 +1,83 @@
+{
+  "components": {
+    "biophysical_neuron_models_dir": "",
+    "mechanisms_dir": "",
+    "morphologies_dir": "",
+    "point_neuron_models_dir": "",
+    "provenance": {
+      "id_mapping": "id_mapping.json"
+    },
+    "synaptic_models_dir": "",
+    "templates_dir": ""
+  },
+  "networks": {
+    "edges": [
+      {
+        "edges_file": "$BASE_DIR/POm__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "POm__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/VPM__S1nonbarrel_neurons__chemical/edges.h5",
+        "populations": {
+          "VPM__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      },
+      {
+        "edges_file": "$BASE_DIR/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical": {
+            "type": "chemical"
+          }
+        }
+      }
+    ],
+    "nodes": [
+      {
+        "nodes_file": "$BASE_DIR/S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "S1nonbarrel_neurons": {
+            "alternate_morphologies": {},
+            "biophysical_neuron_models_dir": "$BASE_DIR/emodels_hoc",
+            "morphologies_dir": "$BASE_DIR/morphologies/swc",
+            "type": "biophysical"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/POm/nodes.h5",
+        "populations": {
+          "POm": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/VPM/nodes.h5",
+        "populations": {
+          "VPM": {
+            "type": "virtual"
+          }
+        }
+      },
+      {
+        "nodes_file": "$BASE_DIR/external_S1nonbarrel_neurons/nodes.h5",
+        "populations": {
+          "external_S1nonbarrel_neurons": {
+            "type": "virtual"
+          }
+        }
+      }
+    ]
+  },
+  "node_sets_file": "$BASE_DIR/node_sets.json",
+  "version": 2.3,
+  "manifest": {
+    "$BASE_DIR": "./"
+  }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/emodels_hoc/cADpyr_L5TPC.hoc b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/emodels_hoc/cADpyr_L5TPC.hoc
new file mode 100644
index 00000000..d9161a4b
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/emodels_hoc/cADpyr_L5TPC.hoc
@@ -0,0 +1,486 @@
+/*
+Created by BluePyOpt(1.9.48) at 2020-07-30 09:19:16.584907
+Updated 'load_morphology' based on 'cell_template_neurodamus_obi.jinja2' by obi(0.1.0) at 2025-04-16 16:18:45.839578
+*/
+{load_file("stdrun.hoc")}
+{load_file("import3d.hoc")}
+/*
+ * Check that global parameters are the same as with the optimization
+ */
+proc check_parameter(/* name, expected_value, value */){
+  strdef error
+  if($2 != $3){
+    sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3)
+    execerror(error)
+  }
+}
+proc check_simulator() {
+  check_parameter("celsius", 34, celsius)
+  check_parameter("v_init", -80, v_init)
+}
+
+begintemplate cADpyr_L5TPC
+  public init, morphology, geom_nseg_fixed, geom_nsec, getCell, getCCell, setCCell, gid, getCell
+  public channel_seed, channel_seed_set
+  public connect2target, clear, ASCIIrpt
+  public soma, dend, apic, axon, myelin, getThreshold
+  create soma[1], dend[1], apic[1], axon[1], myelin[1]
+  public nSecAll, nSecSoma, nSecApical, nSecBasal, nSecMyelinated, nSecAxonalOrig, nSecAxonal
+  public CellRef, synHelperList, synlist
+  objref this, CellRef, segCounts, ASCIIrpt, synHelperList, synlist
+
+  public all, somatic, apical, axonal, basal, myelinated, APC
+  objref all, somatic, apical, axonal, basal, myelinated, APC
+
+
+obfunc getCell(){
+        return this
+}
+
+obfunc getCCell(){
+	return CellRef
+}
+proc setCCell(){
+       CellRef = $o1
+}
+
+//-----------------------------------------------------------------------------------------------
+
+/*!
+ * When clearing the model, the circular reference between Cells and CCells must be broken so the
+ * entity watching reference counts can work.
+ */
+proc clear() { localobj nil
+    CellRef = nil
+}
+
+
+
+/*!
+ * @param $o1 NetCon source (can be nil)
+ * @param $o2 Variable where generated NetCon will be placed
+ */
+proc connect2target() { //$o1 target point process, $o2 returned NetCon
+  soma $o2 = new NetCon(&v(1), $o1)
+  $o2.threshold = -30
+}
+
+
+proc init(/* args: morphology_dir, morphology_name */) {
+  all = new SectionList()
+  apical = new SectionList()
+  axonal = new SectionList()
+  basal = new SectionList()
+  somatic = new SectionList()
+  myelinated = new SectionList()
+
+  synHelperList = new List()                                                     
+  synlist = new List()     
+
+  //For compatibility with BBP CCells
+  CellRef = this
+
+  forall delete_section()
+
+  gid = $1
+
+  if(numarg() >= 3) {
+    load_morphology($s2, $s3)
+  } else {
+    load_morphology($s2, "C060114A5.asc")
+  }
+
+  geom_nseg()
+  indexSections()
+    replace_axon()
+  insertChannel()
+  biophys()
+
+  // Initialize channel_seed_set to avoid accidents
+  channel_seed_set = 0
+  // Initialize random number generators
+  re_init_rng()
+}
+
+/*!
+ * Assign section indices to the section voltage value.  This will be useful later for serializing
+ * the sections into an array.  Note, that once the simulation begins, the voltage values will revert to actual data again.
+ *
+ * @param $o1 Import3d_GUI object
+ */
+proc indexSections() { local index
+    index = 0
+    forsec all {
+        v(0.0001) = index
+        index = index +1
+    }
+}
+
+func getThreshold() { return 0.0 }
+
+proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension, commands, pyobj
+  strdef morph_path, loadstr
+  sprint(morph_path, "%s/%s", $s1, $s2)
+  sf = new StringFunctions()
+  extension = new String()
+  sscanf(morph_path, "%s", extension.s)
+
+  sf.right(extension.s, sf.len(extension.s)-3)
+  if(strcmp(extension.s, "ASC") == 0 ){
+    extension.s = "asc"
+  }
+  if(strcmp(extension.s, "SWC") == 0 ){
+    extension.s = "swc"
+  }
+
+  if( strcmp(extension.s, "asc") == 0 ) {
+     morph = new Import3d_Neurolucida3()
+     morph.quiet = 1
+     morph.input(morph_path)
+
+     import = new Import3d_GUI(morph, 0)
+     import.instantiate(this)
+  } else if( strcmp(extension.s, "swc" ) == 0) {
+     morph = new Import3d_SWC_read()
+     morph.quiet = 1
+     morph.input(morph_path)
+
+     import = new Import3d_GUI(morph, 0)
+     import.instantiate(this)
+  } else if( strcmp(extension.s, ".h5") == 0 ) {
+     sprint( loadstr, "morphio_read( %s, \"%s\" )", this, morph_path )
+     execute1( loadstr )
+     indexSections()
+     geom_nsec()
+  } else {
+    printf(extension.s)
+    printf("Unsupported file format: Morphology file has to end with .asc, .swc or .h5" )
+    quit()
+  }
+}
+
+/*
+ * Assignment of mechanism values based on distance from the soma
+ * Matches the BluePyOpt method
+ */
+proc distribute_distance(){local x localobj sl
+  strdef stmp, distfunc, mech
+
+  sl = $o1
+  mech = $s2
+  distfunc = $s3
+  this.soma[0] distance(0, 0.5)
+  sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
+  forsec sl for(x, 0) {
+    sprint(stmp, distfunc, secname(), x, distance(x))
+    execute(stmp)
+  }
+}
+
+proc geom_nseg() {
+  this.geom_nsec() //To count all sections
+  //TODO: geom_nseg_fixed depends on segCounts which is calculated by
+  //  geom_nsec.  Can this be collapsed?
+  this.geom_nseg_fixed(40)
+  this.geom_nsec() //To count all sections
+}
+
+proc insertChannel() {
+  forsec this.all {
+    insert pas
+  }
+  forsec this.apical {
+    insert CaDynamics_DC0
+    insert Ca_HVA2
+    insert Ca_LVAst
+    insert NaTg
+    insert SKv3_1
+    insert Ih
+  }
+  forsec this.axonal {
+    insert CaDynamics_DC0
+    insert Ca_HVA2
+    insert Ca_LVAst
+    insert SKv3_1
+    insert SK_E2
+    insert K_Pst
+    insert K_Tst
+    insert NaTg
+    insert Nap_Et2
+  }
+  forsec this.basal {
+    insert CaDynamics_DC0
+    insert Ca_HVA2
+    insert Ca_LVAst
+    insert Ih
+  }
+  forsec this.somatic {
+    insert CaDynamics_DC0
+    insert Ca_HVA2
+    insert Ca_LVAst
+    insert SKv3_1
+    insert SK_E2
+    insert K_Pst
+    insert K_Tst
+    insert NaTg
+    insert Ih
+  }
+  forsec this.myelinated {
+  }
+}
+
+proc biophys() {
+  
+  forsec CellRef.all {
+    Ra = 100
+    g_pas = 4.7051946481499811e-05
+    e_pas = -79.603749561469684
+  }
+  
+  forsec CellRef.apical {
+    cm = 2
+    ena = 50
+    ek = -90
+    gamma_CaDynamics_DC0 = 0.04517010601950254
+    vshiftm_NaTg = 6
+    vshifth_NaTg = 6
+    gSKv3_1bar_SKv3_1 = 0.0018178286052065377
+    gCa_HVAbar_Ca_HVA2 = 3.4636229677800608e-06
+    gCa_LVAstbar_Ca_LVAst = 0.00098549473641638421
+  }
+  
+  forsec CellRef.axonal {
+    cm = 1
+    ena = 50
+    ek = -90
+    vshifth_NaTg = 10
+    slopem_NaTg = 9
+    gNaTgbar_NaTg = 0.33556102181366648
+    gNap_Et2bar_Nap_Et2 = 0.0050542837974557036
+    gK_Pstbar_K_Pst = 0.99801140749927841
+    gK_Tstbar_K_Tst = 0.064027490811985444
+    gSKv3_1bar_SKv3_1 = 0.21965904565617991
+    gCa_HVAbar_Ca_HVA2 = 0.00050971480143243212
+    gCa_LVAstbar_Ca_LVAst = 0.0009394854482623271
+    gSK_E2bar_SK_E2 = 0.096808660670667457
+    decay_CaDynamics_DC0 = 285.46128321068517
+    gamma_CaDynamics_DC0 = 0.012752321129750291
+  }
+  
+  forsec CellRef.basal {
+    cm = 2
+    gamma_CaDynamics_DC0 = 0.048834292167029961
+    gCa_HVAbar_Ca_HVA2 = 7.4498164370347347e-05
+    gCa_LVAstbar_Ca_LVAst = 0.00074352999455036864
+  }
+  
+  forsec CellRef.somatic {
+    cm = 1
+    ena = 50
+    ek = -90
+    vshiftm_NaTg = 13
+    vshifth_NaTg = 15
+    slopem_NaTg = 7
+    gNaTgbar_NaTg = 0.28992456437225478
+    gK_Pstbar_K_Pst = 0.10031922891367806
+    gK_Tstbar_K_Tst = 0.013153142654420691
+    gSKv3_1bar_SKv3_1 = 0.38829177538605941
+    gCa_HVAbar_Ca_HVA2 = 0.00083534595739915185
+    gCa_LVAstbar_Ca_LVAst = 0.00021537640631977598
+    gSK_E2bar_SK_E2 = 0.020239349931865241
+    decay_CaDynamics_DC0 = 282.09663415587323
+    gamma_CaDynamics_DC0 = 0.022091065777470664
+  }
+  
+  forsec CellRef.myelinated {
+    cm = 0.02
+  }
+  
+  distribute_distance(CellRef.apical, "gIhbar_Ih", "(-0.8696 + 2.087*exp((%.17g)*0.0031))*2.6113487420290699e-05")
+  distribute_distance(CellRef.apical, "gNaTgbar_NaTg", "exp(%.17g*-0.00453252486077)*0.081013620392016858")
+  distribute_distance(CellRef.basal, "gIhbar_Ih", "(-0.8696 + 2.087*exp((%.17g)*0.0031))*2.6113487420290699e-05")
+  distribute_distance(CellRef.somatic, "gIhbar_Ih", "(-0.8696 + 2.087*exp((%.17g)*0.0031))*2.6113487420290699e-05")
+}
+
+func sec_count(/* SectionList */) { local nSec
+  nSec = 0
+  forsec $o1 {
+      nSec += 1
+  }
+  return nSec
+}
+
+/*
+ * Iterate over the section and compute how many segments should be allocate to
+ * each.
+ */
+proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize
+  chunkSize = $1
+  soma area(.5) // make sure diam reflects 3d points
+  secIndex = 0
+  forsec all {
+    nseg = 1 + 2*int(L/chunkSize)
+    segCounts.x[secIndex] = nseg
+    secIndex += 1
+  }
+}
+
+/*
+ * Count up the number of sections
+ */
+proc geom_nsec() { local nSec
+  nSecAll = sec_count(all)
+  nSecSoma = sec_count(somatic)
+  nSecApical = sec_count(apical)
+  nSecBasal = sec_count(basal)
+  nSecMyelinated = sec_count(myelinated)
+  nSecAxonalOrig = nSecAxonal = sec_count(axonal)
+
+  segCounts = new Vector()
+  segCounts.resize(nSecAll)
+  nSec = 0
+  forsec all {
+    segCounts.x[nSec] = nseg
+    nSec += 1
+  }
+}
+
+/*
+ * Replace the axon built from the original morphology file with a stub axon
+ */
+    
+    proc replace_axon(){ local nSec, L_chunk, dist, i1, i2, count, L_target, chunkSize, L_real localobj diams, lens
+
+        L_target = 60  // length of stub axon
+        nseg0 = 5  // number of segments for each of the two axon sections
+
+        nseg_total = nseg0 * 2
+        chunkSize = L_target/nseg_total
+
+        nSec = 0
+        forsec axonal{nSec = nSec + 1}
+
+        // Try to grab info from original axon
+        if(nSec < 3){ //At least two axon sections have to be present!
+
+            execerror("Less than three axon sections are present! This emodel can't be run with such a morphology!")
+
+        } else {
+
+            diams = new Vector()
+            lens = new Vector()
+
+            access axon[0]
+            axon[0] i1 = v(0.0001) // used when serializing sections prior to sim start
+            axon[1] i2 = v(0.0001) // used when serializing sections prior to sim start
+            axon[2] i3 = v(0.0001) // used when serializing sections prior to sim start
+
+            count = 0
+            forsec axonal{ // loop through all axon sections
+
+                nseg = 1 + int(L/chunkSize/2.)*2  //nseg to get diameter
+
+                for (x) {
+                    if (x > 0 && x < 1) {
+                        count = count + 1
+                        diams.resize(count)
+                        diams.x[count-1] = diam(x)
+                        lens.resize(count)
+                        lens.x[count-1] = L/nseg
+                        if( count == nseg_total ){
+                            break
+                        }
+                    }
+                }
+                if( count == nseg_total ){
+                    break
+                }
+            }
+
+            // get rid of the old axon
+            forsec axonal{delete_section()}
+            execute1("create axon[2]", CellRef)
+
+            L_real = 0
+            count = 0
+
+            // new axon dependant on old diameters
+            for i=0,1{
+                access axon[i]
+                L =  L_target/2
+                nseg = nseg_total/2
+
+                for (x) {
+                    if (x > 0 && x < 1) {
+                        diam(x) = diams.x[count]
+                        L_real = L_real+lens.x[count]
+                        count = count + 1
+                    }
+                }
+
+                all.append()
+                axonal.append()
+
+                if (i == 0) {
+                    v(0.0001) = i1
+                } else {
+                    v(0.0001) = i2
+                }
+            }
+
+            nSecAxonal = 2
+            soma[0] connect axon[0](0), 1
+            axon[0] connect axon[1](0), 1
+
+            create myelin[1]
+            access myelin{
+                    L = 1000
+                    diam = diams.x[count-1]
+                    nseg = 5
+                    v(0.0001) = i3
+                    all.append()
+                    myelinated.append()
+            }
+            connect myelin(0), axon[1](1)
+        }
+    }
+
+
+
+
+func hash_str() {localobj sf strdef right
+  sf = new StringFunctions()
+
+  right = $s1
+
+  n_of_c = sf.len(right)
+
+  hash = 0
+  char_int = 0
+  for i = 0, n_of_c - 1 {
+     sscanf(right, "%c", & char_int)
+     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)
+     sf.right(right, 1)
+  }
+
+  return hash
+}
+
+proc re_init_rng() {localobj sf
+    strdef full_str, name
+
+    sf = new StringFunctions()
+
+    if(numarg() == 1) {
+        // We received a third seed
+        channel_seed = $1
+        channel_seed_set = 1
+    } else {
+        channel_seed_set = 0
+    }
+
+
+}
+
+
+endtemplate cADpyr_L5TPC
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons/nodes.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons/nodes.h5
new file mode 100644
index 00000000..85d2dfad
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons/nodes.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5
new file mode 100644
index 00000000..b91b701e
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/external_S1nonbarrel_neurons__S1nonbarrel_neurons__chemical.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/id_mapping.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/id_mapping.json
new file mode 100644
index 00000000..110544e0
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/id_mapping.json
@@ -0,0 +1,2769 @@
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+    ],
+    "original_name": "S1nonbarrel_neurons"
+  }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/CaDynamics_DC0.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/CaDynamics_DC0.mod
new file mode 100755
index 00000000..0e7867c1
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/CaDynamics_DC0.mod
@@ -0,0 +1,57 @@
+: Dynamics that track inside calcium concentration
+: modified from Destexhe et al. 1994
+: Dan Keller and Christian Roessert - removed shell, made dependent on geometry of individual sections via the surface to volume ratio
+: LJP: OK, correction not necessary, no voltage values
+
+NEURON	{
+	SUFFIX CaDynamics_DC0
+	USEION ca READ ica WRITE cai
+	RANGE decay, gamma
+}
+
+UNITS	{
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+	FARADAY = (faraday) (coulombs)
+	PI      = (pi)       (1)
+	(molar) = (1/liter)
+	(mM) = (millimolar)
+	(um)	= (micron)
+}
+
+PARAMETER	{
+
+	: Percent of free calcium (not buffered), Helmchen et al. 1996: (L5) kappa_e = 100
+	gamma = 0.01 (1)
+
+	: rate of removal of calcium:
+	: L5 proximal dendrite: (Helmchen 1996) 70ms @ 7um diam
+	decay = 70 (ms)
+
+	: baseline calcium
+	: CA1: (Sabatini 2002) 65 nM
+	minCai = 6.5e-5 (mM)
+}
+
+ASSIGNED	{
+	ica (mA/cm2)
+	diam (um)  : diameter of current segment (automatically available within NMODL, like v)
+	surftovol (1/um)
+	}
+
+STATE	{
+	cai (mM)
+	}
+
+BREAKPOINT	{ SOLVE states METHOD cnexp }
+
+INITIAL {
+	: surface = 2*PI*(diam/2)*L
+	: volume = PI*(diam/2)*(diam/2)*L
+	surftovol = 4 / diam
+	cai = minCai
+}
+
+DERIVATIVE states	{
+	cai' = -(10000)*ica*surftovol*gamma/(2*FARADAY) - (cai - minCai)/decay
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_HVA2.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_HVA2.mod
new file mode 100755
index 00000000..e861de76
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_HVA2.mod
@@ -0,0 +1,77 @@
+:Comment :
+:Reference : : Reuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993
+: activation from Sayer, Schwindt and Crill (1990)
+: inactivation from Dichter and Zona 1989
+: LJP: OK, inactivation corrected by 12.3 mV
+
+NEURON	{
+	SUFFIX Ca_HVA2
+	USEION ca READ eca WRITE ica
+	RANGE gCa_HVAbar, gCa_HVA, ica
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gCa_HVAbar = 0.00001 (S/cm2)
+}
+
+ASSIGNED	{
+	v	(mV)
+	eca	(mV)
+	ica	(mA/cm2)
+	gCa	(S/cm2)
+	mInf
+	mTau
+	mAlpha
+	mBeta
+	hInf
+	hTau
+	hAlpha
+	hBeta
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gCa = gCa_HVAbar*m*m*h
+	ica = gCa*(v-eca)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+	UNITSOFF
+        if((v == -27) ){
+            v = v+0.0001
+        }
+		mAlpha =  (0.055*(-27-v))/(exp((-27-v)/3.8) - 1)
+		mBeta  =  (0.94*exp((-75-v)/17))
+		mInf = mAlpha/(mAlpha + mBeta)
+		mTau = 1/(mAlpha + mBeta)
+		v = v + 12.3
+		hAlpha =  (0.000457*exp((-13-v)/50))
+		hBeta  =  (0.0065/(exp((-v-15)/28)+1))
+		hInf = hAlpha/(hAlpha + hBeta)
+		hTau = 1/(hAlpha + hBeta)
+		v = v - 12.3
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_LVAst.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_LVAst.mod
new file mode 100755
index 00000000..345217f5
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ca_LVAst.mod
@@ -0,0 +1,69 @@
+:Comment : LVA ca channel. Note: mtau is an approximation from the plots
+:Reference : :		Avery and Johnston 1996, tau from Randall 1997
+:Comment: shifted by -10 mv to correct for junction potential
+:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21
+: LJP: OK
+
+NEURON	{
+	SUFFIX Ca_LVAst
+	USEION ca READ eca WRITE ica
+	RANGE gCa_LVAstbar, gCa_LVAst, ica
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gCa_LVAstbar = 0.00001 (S/cm2)
+}
+
+ASSIGNED	{
+	v	(mV)
+	eca	(mV)
+	ica	(mA/cm2)
+	gCa_LVAst	(S/cm2)
+	mInf
+	mTau
+	hInf
+	hTau
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gCa_LVAst = gCa_LVAstbar*m*m*h
+	ica = gCa_LVAst*(v-eca)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+  LOCAL qt
+  qt = 2.3^((34-21)/10)
+
+	UNITSOFF
+		v = v + 10
+		mInf = 1.0000/(1+ exp((v - -30.000)/-6))
+		mTau = (5.0000 + 20.0000/(1+exp((v - -25.000)/5)))/qt
+		hInf = 1.0000/(1+ exp((v - -80.000)/6.4))
+		hTau = (20.0000 + 50.0000/(1+exp((v - -40.000)/7)))/qt
+		v = v - 10
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ih.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ih.mod
new file mode 100755
index 00000000..6b906577
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Ih.mod
@@ -0,0 +1,62 @@
+:Comment :
+:Reference : :		Kole,Hallermann,and Stuart, J. Neurosci. 2006
+: LJP: OK, "Somatic whole- cell voltage was corrected for the  14 mV difference" 
+
+NEURON	{
+	SUFFIX Ih
+	NONSPECIFIC_CURRENT ihcn
+	RANGE gIhbar, gIh, ihcn
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gIhbar = 0.00001 (S/cm2)
+	ehcn =  -45.0 (mV)
+}
+
+ASSIGNED	{
+	v	(mV)
+	ihcn	(mA/cm2)
+	gIh	(S/cm2)
+	mInf
+	mTau
+	mAlpha
+	mBeta
+}
+
+STATE	{
+	m
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gIh = gIhbar*m
+	ihcn = gIh*(v-ehcn)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+}
+
+PROCEDURE rates(){
+	UNITSOFF
+        if(v == -154.9){
+            v = v + 0.0001
+        }
+		mAlpha =  0.001*6.43*(v+154.9)/(exp((v+154.9)/11.9)-1)
+		mBeta  =  0.001*193*exp(v/33.1)
+		mInf = mAlpha/(mAlpha + mBeta)
+		mTau = 1/(mAlpha + mBeta)
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Pst.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Pst.mod
new file mode 100755
index 00000000..10fba7bb
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Pst.mod
@@ -0,0 +1,72 @@
+:Comment : The persistent component of the K current
+:Reference : :		Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000
+:Comment : shifted -10 mv to correct for junction potential
+:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21
+: LJP: OK
+
+NEURON	{
+	SUFFIX K_Pst
+	USEION k READ ek WRITE ik
+	RANGE gK_Pstbar, gK_Pst, ik
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gK_Pstbar = 0.00001 (S/cm2)
+}
+
+ASSIGNED	{
+	v	(mV)
+	ek	(mV)
+	ik	(mA/cm2)
+	gK_Pst	(S/cm2)
+	mInf
+	mTau
+	hInf
+	hTau
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gK_Pst = gK_Pstbar*m*m*h
+	ik = gK_Pst*(v-ek)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+  LOCAL qt
+  qt = 2.3^((34-21)/10)
+	UNITSOFF
+		v = v + 10
+		mInf =  (1/(1 + exp(-(v+1)/12)))
+        if(v<-50){
+		    mTau =  (1.25+175.03*exp(-v * -0.026))/qt
+        }else{
+            mTau = ((1.25+13*exp(-v*0.026)))/qt
+        }
+		hInf =  1/(1 + exp(-(v+54)/-11))
+		hTau =  (360+(1010+24*(v+55))*exp(-((v+75)/48)^2))/qt
+		v = v - 10
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Tst.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Tst.mod
new file mode 100755
index 00000000..e922cfcd
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/K_Tst.mod
@@ -0,0 +1,69 @@
+:Comment : The transient component of the K current
+:Reference : :		Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000
+:Comment : shifted -10 mv to correct for junction potential
+:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21
+: LJP: OK
+
+NEURON	{
+	SUFFIX K_Tst
+	USEION k READ ek WRITE ik
+	RANGE gK_Tstbar, gK_Tst, ik
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gK_Tstbar = 0.00001 (S/cm2)
+}
+
+ASSIGNED	{
+	v	(mV)
+	ek	(mV)
+	ik	(mA/cm2)
+	gK_Tst	(S/cm2)
+	mInf
+	mTau
+	hInf
+	hTau
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gK_Tst = gK_Tstbar*(m^4)*h
+	ik = gK_Tst*(v-ek)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+  LOCAL qt
+  qt = 2.3^((34-21)/10)
+
+	UNITSOFF
+		v = v + 10
+		mInf =  1/(1 + exp(-(v+0)/19))
+		mTau =  (0.34+0.92*exp(-((v+71)/59)^2))/qt
+		hInf =  1/(1 + exp(-(v+66)/-10))
+		hTau =  (8+49*exp(-((v+73)/23)^2))/qt
+		v = v - 10
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/KdShu2007.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/KdShu2007.mod
new file mode 100755
index 00000000..2327f116
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/KdShu2007.mod
@@ -0,0 +1,68 @@
+TITLE K-D
+: K-D current for prefrontal cortical neuron ------Yuguo Yu  2007
+: LJP: OK, "liquid junction potential of 10 mV was subtracted from each recording"
+
+NEURON {
+     THREADSAFE
+	SUFFIX KdShu2007
+	USEION k WRITE ik
+	RANGE  gkbar, ik, ek
+	RANGE minf, mtau, hinf, htau
+}
+
+PARAMETER {
+	gkbar = 0.1   	(mho/cm2)
+
+	celsius
+	ek = -100	(mV)            : must be explicitly def. in hoc
+	v 		(mV)
+	vhalfm=-43  (mV)
+	km=8
+	vhalfh=-67  (mV)
+      kh=7.3
+	q10=2.3
+}
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(um) = (micron)
+}
+
+ASSIGNED {
+	ik 		(mA/cm2)
+	minf 		mtau (ms)
+	hinf 		htau (ms)
+}
+
+
+STATE { m h}
+
+BREAKPOINT {
+        SOLVE states METHOD cnexp
+       ik = gkbar * m*h*(v-ek)
+}
+
+INITIAL {
+	trates(v)
+	m=minf
+	h=hinf
+}
+
+DERIVATIVE states {
+        trates(v)
+        m' = (minf-m)/mtau
+        h' = (hinf-h)/htau
+}
+
+PROCEDURE trates(v) {
+	LOCAL qt
+        qt=q10^((celsius-22)/10)
+        minf=1-1/(1+exp((v-vhalfm)/km))
+        hinf=1/(1+exp((v-vhalfh)/kh))
+
+  	 mtau = 0.6
+	 htau = 1500
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/NaTg.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/NaTg.mod
new file mode 100755
index 00000000..07d54c06
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/NaTg.mod
@@ -0,0 +1,104 @@
+:Reference :Colbert and Pan 2002
+
+: Adapted by Werner Van Geit @ BBP, 2015 (with help from M.Hines):
+: channel detects TTX concentration set by TTXDynamicsSwitch.mod
+: LJP: not corrected!
+
+NEURON	{
+	SUFFIX NaTg
+	USEION na READ ena WRITE ina
+	USEION ttx READ ttxo, ttxi VALENCE 1
+	RANGE gNaTgbar, gNaTg, ina, vshifth, vshiftm, slopeh, slopem
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gNaTgbar = 0.00001 (S/cm2)
+	vshifth = 0 (mV)
+	vshiftm = 0 (mV)
+	slopeh = 6
+	slopem = 6
+}
+
+ASSIGNED	{
+	ttxo (mM)
+	ttxi (mM)
+	v	(mV)
+	ena	(mV)
+	ina	(mA/cm2)
+	gNaTg	(S/cm2)
+	mInf
+	mTau
+	mAlpha
+	mBeta
+	hInf
+	hTau
+	hAlpha
+	hBeta
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gNaTg = gNaTgbar*m*m*m*h
+	ina = gNaTg*(v-ena)
+}
+
+DERIVATIVE states	{
+	if (ttxi == 0.015625 && ttxo > 1e-12) {
+		mInf = 0.0
+		mTau = 1e-12
+		hInf = 1.0
+		hTau = 1e-12
+	} else {
+		rates()
+	}
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	if (ttxi == 0.015625 && ttxo > 1e-12) {
+		mInf = 0.0
+		mTau = 1e-12
+		hInf = 1.0
+		hTau = 1e-12
+	} else {
+		rates()
+	}
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+  LOCAL qt
+  qt = 2.3^((34-21)/10)
+
+  UNITSOFF
+    if(v == (-38+vshiftm)){
+    	v = v+0.0001
+    }
+		mAlpha = (0.182 * (v- (-38+vshiftm)))/(1-(exp(-(v- (-38+vshiftm))/slopem)))
+		mBeta  = (0.124 * (-v + (-38+vshiftm)))/(1-(exp(-(-v + (-38+vshiftm))/slopem)))
+		mTau = (1/(mAlpha + mBeta))/qt
+		mInf = mAlpha/(mAlpha + mBeta)
+
+    if(v == (-66+vshifth)){
+      v = v + 0.0001
+    }
+
+		hAlpha = (-0.015 * (v- (-66+vshifth)))/(1-(exp((v- (-66+vshifth))/slopeh)))
+		hBeta  = (-0.015 * (-v +(-66+vshifth)))/(1-(exp((-v +(-66+vshifth))/slopeh)))
+		hTau = (1/(hAlpha + hBeta))/qt
+		hInf = hAlpha/(hAlpha + hBeta)
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Nap_Et2.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Nap_Et2.mod
new file mode 100755
index 00000000..72f0ec6e
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/Nap_Et2.mod
@@ -0,0 +1,108 @@
+:Comment : mtau deduced from text (said to be 6 times faster than for NaTa)
+:Comment : so I used the equations from NaT and multiplied by 6
+:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999
+:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21
+
+: Adapted by Werner Van Geit @ BBP, 2015 (with help from M.Hines):
+: channel detects TTX concentration set by TTXDynamicsSwitch.mod
+
+: LJP: OK, cell-attached modes bath and pipette solution almost identical ljp < 1mV "this procedure can be considered as valid in the case of a cell-attached or inside-out patch measurement provided that the pipette contains a solution identical to that of the bath" Erwin Neher, 1992
+
+
+NEURON {
+	SUFFIX Nap_Et2
+	USEION na READ ena WRITE ina
+	USEION ttx READ ttxo, ttxi VALENCE 1
+	RANGE gNap_Et2bar, gNap_Et2, ina
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gNap_Et2bar = 0.00001 (S/cm2)
+}
+
+ASSIGNED {
+	ttxo (mM)
+	ttxi (mM)
+	v	(mV)
+	ena	(mV)
+	ina	(mA/cm2)
+	gNap_Et2	(S/cm2)
+	mInf
+	mTau
+	mAlpha
+	mBeta
+	hInf
+	hTau
+	hAlpha
+	hBeta
+}
+
+STATE	{
+	m
+	h
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gNap_Et2 = gNap_Et2bar*m*m*m*h
+	ina = gNap_Et2*(v-ena)
+}
+
+DERIVATIVE states	{
+	if (ttxi == 0.015625 && ttxo > 1e-12) {
+		mInf = 0.0
+		mTau = 1e-12
+		hInf = 1.0
+		hTau = 1e-12
+	} else {
+		rates()
+	}
+	m' = (mInf-m)/mTau
+	h' = (hInf-h)/hTau
+}
+
+INITIAL{
+	if (ttxi == 0.015625 && ttxo > 1e-12) {
+		mInf = 0.0
+		mTau = 1e-12
+		hInf = 1.0
+		hTau = 1e-12
+	} else {
+		rates()
+	}
+	m = mInf
+	h = hInf
+}
+
+PROCEDURE rates(){
+  LOCAL qt
+  qt = 2.3^((34-21)/10)
+
+	UNITSOFF
+		mInf = 1.0/(1+exp((v- -52.6)/-4.6))
+    if(v == -38){
+    	v = v+0.0001
+    }
+		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
+		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
+		mTau = 6*(1/(mAlpha + mBeta))/qt
+
+  	if(v == -17){
+   		v = v + 0.0001
+  	}
+    if(v == -64.4){
+      v = v+0.0001
+    }
+
+		hInf = 1.0/(1+exp((v- -48.8)/10))
+    hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63))
+    hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63))
+		hTau = (1/(hAlpha + hBeta))/qt
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbAMPANMDA_EMS.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbAMPANMDA_EMS.mod
new file mode 100755
index 00000000..d0ff60f3
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbAMPANMDA_EMS.mod
@@ -0,0 +1,606 @@
+COMMENT
+/**
+ * @file ProbAMPANMDA_EMS.mod
+ * @brief
+ * @author king, muller, reimann, ramaswamy
+ * @date 2011-08-17
+ * @remark Copyright © BBP/EPFL 2005-2011; All rights reserved. Do not distribute without further notice.
+ */
+ENDCOMMENT
+
+TITLE Probabilistic AMPA and NMDA receptor with presynaptic short-term plasticity
+
+
+COMMENT
+AMPA and NMDA receptor conductance using a dual-exponential profile
+presynaptic short-term plasticity as in Fuhrmann et al. 2002
+
+_EMS (Eilif Michael Srikanth)
+Modification of ProbAMPANMDA: 2-State model by Eilif Muller, Michael Reimann, Srikanth Ramaswamy, Blue Brain Project, August 2011
+This new model was motivated by the following constraints:
+
+1) No consumption on failure.
+2) No release just after release until recovery.
+3) Same ensemble averaged trace as deterministic/canonical Tsodyks-Markram
+   using same parameters determined from experiment.
+4) Same quantal size as present production probabilistic model.
+
+To satisfy these constaints, the synapse is implemented as a
+uni-vesicular (generalization to multi-vesicular should be
+straight-forward) 2-state Markov process.  The states are
+{1=recovered, 0=unrecovered}.
+
+For a pre-synaptic spike or external spontaneous release trigger
+event, the synapse will only release if it is in the recovered state,
+and with probability u (which follows facilitation dynamics).  If it
+releases, it will transition to the unrecovered state.  Recovery is as
+a Poisson process with rate 1/Dep.
+
+This model satisys all of (1)-(4).
+
+ENDCOMMENT
+
+
+NEURON {
+    THREADSAFE
+    POINT_PROCESS ProbAMPANMDA_EMS
+
+    GLOBAL tau_r_AMPA, tau_r_NMDA, tau_d_NMDA
+    RANGE tau_d_AMPA
+    RANGE Use, u, Dep, Fac, u0, mg, tsyn
+    RANGE unoccupied, occupied, Nrrp
+
+    GLOBAL slope_mg, scale_mg, e
+    RANGE i_AMPA, i_NMDA, g_AMPA, g_NMDA, g, NMDA_ratio
+    RANGE A_AMPA_step, B_AMPA_step, A_NMDA_step, B_NMDA_step
+
+    NONSPECIFIC_CURRENT i
+    BBCOREPOINTER rng
+    RANGE synapseID, selected_for_report, verboseLevel, conductance
+    RANGE next_delay
+    BBCOREPOINTER delay_times, delay_weights
+    GLOBAL nc_type_param
+    GLOBAL minis_single_vesicle
+    GLOBAL init_depleted
+
+    :RANGE sgid, tgid  : For debugging
+}
+
+PARAMETER {
+    tau_r_AMPA = 0.2      (ms)  : dual-exponential conductance profile
+    tau_d_AMPA = 1.7      (ms)  : IMPORTANT: tau_r < tau_d
+    tau_r_NMDA = 0.29     (ms)  : dual-exponential conductance profile
+    tau_d_NMDA = 43       (ms)  : IMPORTANT: tau_r < tau_d
+    Use = 1.0             (1)   : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values)
+    Dep = 100             (ms)  : relaxation time constant from depression
+    Fac = 10              (ms)  : relaxation time constant from facilitation
+    e   = 0               (mV)  : AMPA and NMDA reversal potential
+    mg  = 1               (mM)  : initial concentration of mg2+
+    slope_mg = 0.07207477 (/mV)
+    scale_mg = 2.5522415  (mM)
+    gmax = .001           (uS)  : weight conversion factor (from nS to uS)
+    u0   = 0                  : initial value of u, which is the running value of release probability
+    Nrrp = 1              (1)   : Number of total release sites for given contact
+    synapseID = 0
+    verboseLevel = 0
+    selected_for_report = 0
+    NMDA_ratio = 0.71     (1)   : The ratio of NMDA to AMPA
+    conductance = 0.0
+    nc_type_param = 4
+    minis_single_vesicle = 0    : // 0 - no limit (old behavior)
+    init_depleted = 0           : // 0 - init full (old behavior)
+}
+
+COMMENT
+The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1
+for comparison with Pr to decide whether to activate the synapse or not
+ENDCOMMENT
+
+VERBATIM
+
+#include<stdlib.h>
+#include<stdio.h>
+#include<math.h>
+#ifndef NRN_VERSION_GTEQ_8_2_0
+#include "nrnran123.h"
+
+#ifndef CORENEURON_BUILD
+extern int ifarg(int iarg);
+
+extern void* vector_arg(int iarg);
+extern double* vector_vec(void* vv);
+extern int vector_capacity(void* vv);
+#endif
+
+double nrn_random_pick(void* r);
+void* nrn_random_arg(int argpos);
+#define RANDCAST
+#else
+#define RANDCAST (Rand*)
+#endif
+
+ENDVERBATIM
+
+
+ASSIGNED {
+    v (mV)
+    i (nA)
+    i_AMPA (nA)
+    i_NMDA (nA)
+    g_AMPA (uS)
+    g_NMDA (uS)
+    g (uS)
+    factor_AMPA
+    factor_NMDA
+    A_AMPA_step
+    B_AMPA_step
+    A_NMDA_step
+    B_NMDA_step
+
+    rng
+    mggate
+    usingR123            : TEMPORARY until mcellran4 completely deprecated
+
+    : MVR
+    unoccupied (1) : no. of unoccupied sites following release event
+    occupied   (1) : no. of occupied sites following one epoch of recovery
+    tsyn (ms) : the time of the last spike
+    u (1) : running release probability
+
+    : stuff for delayed connections
+    delay_times
+    delay_weights
+    next_delay (ms)
+}
+
+PROCEDURE setup_delay_vecs() {
+VERBATIM
+#ifndef CORENEURON_BUILD
+    IvocVect** vv_delay_times = (IvocVect**)(&_p_delay_times);
+    IvocVect** vv_delay_weights = (IvocVect**)(&_p_delay_weights);
+    *vv_delay_times = (IvocVect*)NULL;
+    *vv_delay_weights = (IvocVect*)NULL;
+    if (ifarg(1)) {
+        *vv_delay_times = vector_arg(1);
+    }
+    if (ifarg(2)) {
+        *vv_delay_weights = vector_arg(2);
+    }
+#endif
+ENDVERBATIM
+}
+
+
+STATE {
+    A_AMPA       : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_r_AMPA
+    B_AMPA       : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_d_AMPA
+    A_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_r_NMDA
+    B_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_d_NMDA
+}
+
+
+INITIAL {
+    LOCAL tp_AMPA, tp_NMDA
+
+    tsyn = 0
+    u=u0
+
+    : MVR
+    if ( init_depleted ) {
+        unoccupied = Nrrp
+        occupied = 0
+     } else {
+        unoccupied = 0
+        occupied = Nrrp
+    }
+
+    A_AMPA = 0
+    B_AMPA = 0
+
+    A_NMDA = 0
+    B_NMDA = 0
+
+    tp_AMPA = (tau_r_AMPA*tau_d_AMPA)/(tau_d_AMPA-tau_r_AMPA)*log(tau_d_AMPA/tau_r_AMPA) :time to peak of the conductance
+    tp_NMDA = (tau_r_NMDA*tau_d_NMDA)/(tau_d_NMDA-tau_r_NMDA)*log(tau_d_NMDA/tau_r_NMDA) :time to peak of the conductance
+
+    factor_AMPA = -exp(-tp_AMPA/tau_r_AMPA)+exp(-tp_AMPA/tau_d_AMPA) :AMPA Normalization factor - so that when t = tp_AMPA, gsyn = gpeak
+    factor_AMPA = 1/factor_AMPA
+
+    factor_NMDA = -exp(-tp_NMDA/tau_r_NMDA)+exp(-tp_NMDA/tau_d_NMDA) :NMDA Normalization factor - so that when t = tp_NMDA, gsyn = gpeak
+    factor_NMDA = 1/factor_NMDA
+
+    A_AMPA_step = exp(dt*(( - 1.0 ) / tau_r_AMPA))
+    B_AMPA_step = exp(dt*(( - 1.0 ) / tau_d_AMPA))
+    A_NMDA_step = exp(dt*(( - 1.0 ) / tau_r_NMDA))
+    B_NMDA_step = exp(dt*(( - 1.0 ) / tau_d_NMDA))
+
+    VERBATIM
+        if( usingR123 ) {
+            nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0);
+        }
+    ENDVERBATIM
+
+    next_delay = -1
+}
+
+BREAKPOINT {
+    SOLVE state
+
+    : Jahr and Stevens (1990) model fitted on cortical data from Vargas-Caballero and Robinson (2003).
+    mggate = 1 / (1 + exp(slope_mg * -(v)) * (mg / scale_mg))
+    g_AMPA = gmax*(B_AMPA-A_AMPA) :compute time varying conductance as the difference of state variables B_AMPA and A_AMPA
+    g_NMDA = gmax*(B_NMDA-A_NMDA) * mggate :compute time varying conductance as the difference of state variables B_NMDA and A_NMDA and mggate kinetics
+    g = g_AMPA + g_NMDA
+    i_AMPA = g_AMPA*(v-e) :compute the AMPA driving force based on the time varying conductance, membrane potential, and AMPA reversal
+    i_NMDA = g_NMDA*(v-e) :compute the NMDA driving force based on the time varying conductance, membrane potential, and NMDA reversal
+    i = i_AMPA + i_NMDA
+}
+
+PROCEDURE state() {
+    A_AMPA = A_AMPA*A_AMPA_step
+    B_AMPA = B_AMPA*B_AMPA_step
+    A_NMDA = A_NMDA*A_NMDA_step
+    B_NMDA = B_NMDA*B_NMDA_step
+}
+
+
+NET_RECEIVE (weight, weight_AMPA, weight_NMDA, Psurv, nc_type) {
+    : Psurv - survival probability of unrecovered state
+    : nc_type:
+    :   0 = presynaptic netcon
+    :   1 = spontmini netcon
+    :   2 = replay netcon
+
+    LOCAL result, ves, occu
+    weight_AMPA = weight
+    weight_NMDA = weight * NMDA_ratio
+
+    INITIAL {
+        if (nc_type == 0) {  :// presynaptic netcon
+    VERBATIM
+        // setup self events for delayed connections to change weights
+        IvocVect *vv_delay_times = *((IvocVect**)(&_p_delay_times));
+        IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+        if (vv_delay_times && vector_capacity(vv_delay_times)>=1) {
+            double* deltm_el = vector_vec(vv_delay_times);
+            int delay_times_idx;
+            next_delay = 0;
+            for (delay_times_idx = 0; delay_times_idx < vector_capacity(vv_delay_times); ++delay_times_idx) {
+                double next_delay_t = deltm_el[delay_times_idx];
+    ENDVERBATIM
+            net_send(next_delay_t, 1)
+    VERBATIM
+                }
+            }
+    ENDVERBATIM
+        }
+    }
+    if (flag == 1) {  :// self event to set next weight at
+    VERBATIM
+        // setup self events for delayed connections to change weights
+        IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+        if (vv_delay_weights && vector_capacity(vv_delay_weights)>=next_delay) {
+            double* weights_v = vector_vec(vv_delay_weights);
+            double next_delay_weight = weights_v[(int)next_delay];
+    ENDVERBATIM
+            weight = conductance * next_delay_weight
+            next_delay = next_delay + 1
+    VERBATIM
+        }
+        return;
+    ENDVERBATIM
+    }
+
+    : [flag == 0] Handle a spike which arrived
+    :UNITSOFF
+    :printf("[Syn %.0f] Received! (%f -> %f) with weight %g at time %g\n", synapseID, sgid, tgid, weight, t)
+    :UNITSON
+
+    : Do not perform any calculations if the synapse (netcon) is deactivated. This avoids drawing from
+    : random number stream. Also, disable in case of t < 0 (in case of ForwardSkip) which causes numerical
+    : instability if synapses are activated.
+    if ( weight <= 0 || t < 0 ) {
+    VERBATIM
+        return;
+    ENDVERBATIM
+    }
+
+    : calc u at event-
+    if (Fac > 0) {
+        u = u * exp(-(t - tsyn)/Fac)  :// update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
+    } else {
+        u = Use
+    }
+    if(Fac > 0){
+        u = u + Use*(1-u)  :// update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
+    }
+
+    : recovery
+    FROM counter = 0 TO (unoccupied - 1) {
+        : Iterate over all unoccupied sites and compute how many recover
+        Psurv = exp(-(t-tsyn)/Dep)
+        result = urand()
+        if (result>Psurv) {
+            occupied = occupied + 1     :// recover a previously unoccupied site
+            if ( verboseLevel > 0 ) {
+                UNITSOFF
+                printf("[Syn %.0f] Recovered! t = %g, Psurv = %g, urand = %g\n", synapseID, t, Psurv, result)
+                UNITSON
+            }
+        }
+    }
+
+    ves = 0                  :// Initialize the number of released vesicles to 0
+    occu = occupied          :// Make a copy, so we can update occupied in the loop
+    if (occu > 1 && minis_single_vesicle && nc_type == 1) {    : // if nc_type is spont_mini consider single vesicle
+        occu = 1
+    }
+    FROM counter = 0 TO (occu - 1) {
+        : iterate over all occupied sites and compute how many release
+        result = urand()
+        if (result < u) {
+            : release a single site!
+            occupied = occupied - 1  :// decrease the number of occupied sites by 1
+            ves = ves + 1            :// increase number of relesed vesicles by 1
+        }
+    }
+
+    : Update number of unoccupied sites
+    unoccupied = Nrrp - occupied
+
+    : Update tsyn
+    : tsyn knows about all spikes, not only those that released
+    : i.e. each spike can increase the u, regardless of recovered state.
+    :      and each spike trigger an evaluation of recovery
+    tsyn = t
+
+    if (ves > 0) { :no need to evaluate unless we have vesicle release
+        A_AMPA = A_AMPA + ves/Nrrp*weight_AMPA*factor_AMPA
+        B_AMPA = B_AMPA + ves/Nrrp*weight_AMPA*factor_AMPA
+        A_NMDA = A_NMDA + ves/Nrrp*weight_NMDA*factor_NMDA
+        B_NMDA = B_NMDA + ves/Nrrp*weight_NMDA*factor_NMDA
+
+        if ( verboseLevel > 0 ) {
+            UNITSOFF
+            printf("[Syn %.0f] Release! t = %g, vals: %g %g %g %g\n",
+                   synapseID, t, A_AMPA, weight_AMPA, factor_AMPA, weight)
+            UNITSON
+        }
+
+    } else {
+        : total release failure
+        if ( verboseLevel > 0 ) {
+            UNITSOFF
+            printf("[Syn %.0f] Failure! t = %g, urand = %g\n", synapseID, t, result)
+            UNITSON
+        }
+    }
+}
+
+
+PROCEDURE setRNG() {
+VERBATIM
+    #ifndef CORENEURON_BUILD
+    // For compatibility, allow for either MCellRan4 or Random123
+    // Distinguish by the arg types
+    // Object => MCellRan4, seeds (double) => Random123
+    usingR123 = 0;
+    if( ifarg(1) && hoc_is_double_arg(1) ) {
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        uint32_t a2 = 0;
+        uint32_t a3 = 0;
+
+        if (*pv) {
+            nrnran123_deletestream(*pv);
+            *pv = (nrnran123_State*)0;
+        }
+        if (ifarg(2)) {
+            a2 = (uint32_t)*getarg(2);
+        }
+        if (ifarg(3)) {
+            a3 = (uint32_t)*getarg(3);
+        }
+        *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3);
+        usingR123 = 1;
+    } else if( ifarg(1) ) {   // not a double, so assume hoc object type
+        void** pv = (void**)(&_p_rng);
+        *pv = nrn_random_arg(1);
+    } else {  // no arg, so clear pointer
+        void** pv = (void**)(&_p_rng);
+        *pv = (void*)0;
+    }
+    #endif
+ENDVERBATIM
+}
+
+
+PROCEDURE clearRNG() {
+VERBATIM
+    #ifndef CORENEURON_BUILD
+    if (usingR123) {
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        if (*pv) {
+            nrnran123_deletestream(*pv);
+            *pv = (nrnran123_State*)0;
+        }
+    } else {
+        void** pv = (void**)(&_p_rng);
+        if (*pv) {
+            *pv = (void*)0;
+        }
+    }
+    #endif
+ENDVERBATIM
+}
+
+
+FUNCTION urand() {
+VERBATIM
+    double value = 0.0;
+    if ( usingR123 ) {
+        value = nrnran123_dblpick((nrnran123_State*)_p_rng);
+    } else if (_p_rng) {
+        #ifndef CORENEURON_BUILD
+        value = nrn_random_pick(RANDCAST _p_rng);
+        #endif
+    } else {
+        // Note: prior versions used scop_random(1), but since we never use this model without configuring the rng.  Maybe should throw error?
+        value = 0.0;
+    }
+    _lurand = value;
+ENDVERBATIM
+}
+
+
+FUNCTION bbsavestate() {
+        bbsavestate = 0
+VERBATIM
+#ifndef CORENEURON_BUILD
+        /* first arg is direction (0 save, 1 restore), second is array*/
+        /* if first arg is -1, fill xdir with the size of the array */
+        double *xdir, *xval;
+#ifndef NRN_VERSION_GTEQ_8_2_0
+        double *hoc_pgetarg();
+        long nrn_get_random_sequence(void* r);
+        void nrn_set_random_sequence(void* r, int val);
+#endif
+        xdir = hoc_pgetarg(1);
+        xval = hoc_pgetarg(2);
+        if (_p_rng) {
+            // tell how many items need saving
+            if (*xdir == -1) {  // count items
+                if( usingR123 ) {
+                    *xdir = 2.0;
+                } else {
+                    *xdir = 1.0;
+                }
+                return 0.0;
+            } else if(*xdir ==0 ) {  // save
+                if( usingR123 ) {
+                    uint32_t seq;
+                    char which;
+                    nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which );
+                    xval[0] = (double) seq;
+                    xval[1] = (double) which;
+                } else {
+                    xval[0] = (double)nrn_get_random_sequence(RANDCAST _p_rng);
+                }
+            } else {  // restore
+                if( usingR123 ) {
+                    nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[0], (char)xval[1] );
+                } else {
+                    nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[0]));
+                }
+            }
+        }
+#endif
+ENDVERBATIM
+}
+
+FUNCTION toggleVerbose() {
+    verboseLevel = 1 - verboseLevel
+}
+
+
+VERBATIM
+static void bbcore_write(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
+  IvocVect *vv_delay_times = *((IvocVect**)(&_p_delay_times));
+  IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+
+  if (d) {
+    uint32_t* di = ((uint32_t*)d) + *d_offset;
+    nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+    nrnran123_getids3(*pv, di, di+1, di+2);
+
+    char which;
+    nrnran123_getseq(*pv, di+3, &which);
+    di[4] = (int)which;
+    //printf("ProbAMPANMDA_EMS bbcore_write %d %d %d\n", di[0], di[1], di[2]);
+
+  }
+  // reserve random123 parameters on serialization buffer
+  *d_offset += 5;
+
+  // serialize connection delay vectors
+  if (vv_delay_times && vv_delay_weights &&
+     (vector_capacity(vv_delay_times) >= 1) && (vector_capacity(vv_delay_weights) >= 1)) {
+    if (d) {
+      uint32_t* di = ((uint32_t*)d) + *d_offset;
+      // store vector sizes for deserialization
+      di[0] = vector_capacity(vv_delay_times);
+      di[1] = vector_capacity(vv_delay_weights);
+    }
+    if (x) {
+      double* delay_times_el = vector_vec(vv_delay_times);
+      double* delay_weights_el = vector_vec(vv_delay_weights);
+      double* x_i = x + *x_offset;
+      int delay_vecs_idx;
+      int x_idx = 0;
+      for(delay_vecs_idx = 0; delay_vecs_idx < vector_capacity(vv_delay_times); ++delay_vecs_idx) {
+         x_i[x_idx++] = delay_times_el[delay_vecs_idx];
+         x_i[x_idx++] = delay_weights_el[delay_vecs_idx];
+      }
+    }
+    // reserve space for connection delay data on serialization buffer
+    *x_offset += vector_capacity(vv_delay_times) + vector_capacity(vv_delay_weights);
+  } else {
+    if (d) {
+      uint32_t* di = ((uint32_t*)d) + *d_offset;
+      di[0] = 0;
+      di[1] = 0;
+    }
+
+  }
+  // reserve space for delay vectors (may be 0)
+  *d_offset += 2;
+
+}
+
+static void bbcore_read(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
+  // deserialize random123 data
+  uint32_t* di = ((uint32_t*)d) + *d_offset;
+  if (di[0] != 0 || di[1] != 0 || di[2] != 0) {
+      nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+#if !NRNBBCORE
+      if(*pv) {
+          nrnran123_deletestream(*pv);
+      }
+#endif
+      *pv = nrnran123_newstream3(di[0], di[1], di[2]);
+      char which = (char)di[4];
+      nrnran123_setseq(*pv, di[3], which);
+  }
+  //printf("ProbAMPANMDA_EMS bbcore_read %d %d %d\n", di[0], di[1], di[2]);
+
+  int delay_times_sz = di[5];
+  int delay_weights_sz = di[6];
+  *d_offset += 7;
+
+  if ((delay_times_sz > 0) && (delay_weights_sz > 0)) {
+    double* x_i = x + *x_offset;
+
+    // allocate vectors
+    if (!_p_delay_times) {
+      _p_delay_times = (double*)vector_new1(delay_times_sz);
+    }
+    assert(delay_times_sz == vector_capacity((IvocVect*)_p_delay_times));
+    if (!_p_delay_weights) {
+      _p_delay_weights = (double*)vector_new1(delay_weights_sz);
+    }
+    assert(delay_weights_sz == vector_capacity((IvocVect*)_p_delay_weights));
+
+    double* delay_times_el = vector_vec((IvocVect*)_p_delay_times);
+    double* delay_weights_el = vector_vec((IvocVect*)_p_delay_weights);
+
+    // copy data
+    int x_idx;
+    int vec_idx = 0;
+    for(x_idx = 0; x_idx < delay_times_sz + delay_weights_sz; x_idx += 2) {
+      delay_times_el[vec_idx] = x_i[x_idx];
+      delay_weights_el[vec_idx++] = x_i[x_idx+1];
+    }
+    *x_offset += delay_times_sz + delay_weights_sz;
+
+  }
+}
+ENDVERBATIM
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbGABAAB_EMS.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbGABAAB_EMS.mod
new file mode 100755
index 00000000..a953a2f0
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/ProbGABAAB_EMS.mod
@@ -0,0 +1,611 @@
+COMMENT
+/**
+ * @file ProbGABAAB.mod
+ * @brief
+ * @author king, muller
+ * @date 2011-08-17
+ * @remark Copyright © BBP/EPFL 2005-2011; All rights reserved. Do not distribute without further notice.
+ */
+ENDCOMMENT
+
+TITLE GABAAB receptor with presynaptic short-term plasticity
+
+
+COMMENT
+GABAA receptor conductance using a dual-exponential profile
+presynaptic short-term plasticity based on Fuhrmann et al, 2002
+Implemented by Srikanth Ramaswamy, Blue Brain Project, March 2009
+
+_EMS (Eilif Michael Srikanth)
+Modification of ProbGABAA: 2-State model by Eilif Muller, Michael Reimann, Srikanth Ramaswamy, Blue Brain Project, August 2011
+This new model was motivated by the following constraints:
+
+1) No consumption on failure.
+2) No release just after release until recovery.
+3) Same ensemble averaged trace as deterministic/canonical Tsodyks-Markram
+   using same parameters determined from experiment.
+4) Same quantal size as present production probabilistic model.
+
+To satisfy these constaints, the synapse is implemented as a
+uni-vesicular (generalization to multi-vesicular should be
+straight-forward) 2-state Markov process.  The states are
+{1=recovered, 0=unrecovered}.
+
+For a pre-synaptic spike or external spontaneous release trigger
+event, the synapse will only release if it is in the recovered state,
+and with probability u (which follows facilitation dynamics).  If it
+releases, it will transition to the unrecovered state.  Recovery is as
+a Poisson process with rate 1/Dep.
+
+This model satisys all of (1)-(4).
+
+ENDCOMMENT
+
+
+NEURON {
+    THREADSAFE
+    POINT_PROCESS ProbGABAAB_EMS
+
+    RANGE tau_r_GABAA, tau_d_GABAA
+    GLOBAL tau_r_GABAB, tau_d_GABAB
+    RANGE Use, u, Dep, Fac, u0, tsyn
+    RANGE unoccupied, occupied, Nrrp
+
+    RANGE i, i_GABAA, i_GABAB, g_GABAA, g_GABAB, g, e_GABAA, e_GABAB, GABAB_ratio
+    RANGE A_GABAA_step, B_GABAA_step, A_GABAB_step, B_GABAB_step
+
+    NONSPECIFIC_CURRENT i
+    BBCOREPOINTER rng
+    RANGE synapseID, selected_for_report, verboseLevel, conductance
+    RANGE next_delay
+    BBCOREPOINTER delay_times, delay_weights
+    GLOBAL nc_type_param
+    GLOBAL minis_single_vesicle
+    GLOBAL init_depleted
+
+    :RANGE sgid, tgid  : For debugging
+}
+
+PARAMETER {
+    tau_r_GABAA = 0.2   (ms)  : dual-exponential conductance profile
+    tau_d_GABAA = 8     (ms)  : IMPORTANT: tau_r < tau_d
+    tau_r_GABAB = 3.5   (ms)  : dual-exponential conductance profile :Placeholder value from hippocampal recordings SR
+    tau_d_GABAB = 260.9 (ms)  : IMPORTANT: tau_r < tau_d  :Placeholder value from hippocampal recordings
+    Use = 1.0           (1)   : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values)
+    Dep = 100           (ms)  : relaxation time constant from depression
+    Fac = 10            (ms)  : relaxation time constant from facilitation
+    e_GABAA = -80       (mV)  : GABAA reversal potential
+    e_GABAB = -97       (mV)  : GABAB reversal potential
+    gmax = .001         (uS)  : weight conversion factor (from nS to uS)
+    u0   = 0                  : initial value of u, which is the running value of release probability
+    Nrrp = 1            (1)   : Number of total release sites for given contact
+
+    synapseID = 0
+    verboseLevel = 0
+    selected_for_report = 0
+    GABAB_ratio = 0     (1)   : The ratio of GABAB to GABAA
+    conductance = 0.0
+    nc_type_param = 4
+    minis_single_vesicle = 0   :// 0 - no limit (old behavior)
+    init_depleted = 0          :// 0 - init full (old behavior)
+}
+
+COMMENT
+The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1
+for comparison with Pr to decide whether to activate the synapse or not
+ENDCOMMENT
+
+VERBATIM
+
+#include<stdlib.h>
+#include<stdio.h>
+#include<math.h>
+#ifndef NRN_VERSION_GTEQ_8_2_0
+#include "nrnran123.h"
+
+#ifndef CORENEURON_BUILD
+extern int ifarg(int iarg);
+
+extern void* vector_arg(int iarg);
+extern double* vector_vec(void* vv);
+extern int vector_capacity(void* vv);
+#endif
+
+double nrn_random_pick(void* r);
+void* nrn_random_arg(int argpos);
+#define RANDCAST
+#else
+#define RANDCAST (Rand*)
+#endif
+
+ENDVERBATIM
+
+
+ASSIGNED {
+        v (mV)
+        i (nA)
+        i_GABAA (nA)
+        i_GABAB (nA)
+        g_GABAA (uS)
+        g_GABAB (uS)
+        g (uS)
+        factor_GABAA
+        factor_GABAB
+        A_GABAA_step
+        B_GABAA_step
+        A_GABAB_step
+        B_GABAB_step
+
+        rng
+        usingR123            : TEMPORARY until mcellran4 completely deprecated
+
+        : MVR
+        unoccupied (1) : no. of unoccupied sites following release event
+        occupied   (1) : no. of occupied sites following one epoch of recovery
+        tsyn (ms) : the time of the last spike
+        u (1) : running release probability
+
+        : stuff for delayed connections
+        delay_times
+        delay_weights
+        next_delay (ms)
+}
+
+STATE {
+        A_GABAA       : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAA
+        B_GABAA       : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAA
+        A_GABAB       : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAB
+        B_GABAB       : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAB
+}
+
+
+INITIAL {
+        LOCAL tp_GABAA, tp_GABAB
+
+        tsyn = 0
+        u=u0
+
+        : MVR
+        if ( init_depleted ) {
+            unoccupied = Nrrp
+            occupied = 0
+        } else {
+            unoccupied = 0
+            occupied = Nrrp
+        }
+
+        A_GABAA = 0
+        B_GABAA = 0
+
+        A_GABAB = 0
+        B_GABAB = 0
+
+        tp_GABAA = (tau_r_GABAA*tau_d_GABAA)/(tau_d_GABAA-tau_r_GABAA)*log(tau_d_GABAA/tau_r_GABAA) :time to peak of the conductance
+        tp_GABAB = (tau_r_GABAB*tau_d_GABAB)/(tau_d_GABAB-tau_r_GABAB)*log(tau_d_GABAB/tau_r_GABAB) :time to peak of the conductance
+
+        factor_GABAA = -exp(-tp_GABAA/tau_r_GABAA)+exp(-tp_GABAA/tau_d_GABAA) :GABAA Normalization factor - so that when t = tp_GABAA, gsyn = gpeak
+        factor_GABAA = 1/factor_GABAA
+
+        factor_GABAB = -exp(-tp_GABAB/tau_r_GABAB)+exp(-tp_GABAB/tau_d_GABAB) :GABAB Normalization factor - so that when t = tp_GABAB, gsyn = gpeak
+        factor_GABAB = 1/factor_GABAB
+
+        A_GABAA_step = exp(dt*(( - 1.0 ) / tau_r_GABAA))
+        B_GABAA_step = exp(dt*(( - 1.0 ) / tau_d_GABAA))
+        A_GABAB_step = exp(dt*(( - 1.0 ) / tau_r_GABAB))
+        B_GABAB_step = exp(dt*(( - 1.0 ) / tau_d_GABAB))
+
+    VERBATIM
+        if( usingR123 ) {
+            nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0);
+        }
+    ENDVERBATIM
+
+        next_delay = -1
+
+}
+
+PROCEDURE setup_delay_vecs() {
+VERBATIM
+#ifndef CORENEURON_BUILD
+    void** vv_delay_times = (void**)(&_p_delay_times);
+    void** vv_delay_weights = (void**)(&_p_delay_weights);
+    *vv_delay_times = (void*)NULL;
+    *vv_delay_weights = (void*)NULL;
+    if (ifarg(1)) {
+        *vv_delay_times = vector_arg(1);
+    }
+    if (ifarg(2)) {
+        *vv_delay_weights = vector_arg(2);
+    }
+#endif
+ENDVERBATIM
+}
+
+
+BREAKPOINT {
+        SOLVE state
+
+        g_GABAA = gmax*(B_GABAA-A_GABAA) :compute time varying conductance as the difference of state variables B_GABAA and A_GABAA
+        g_GABAB = gmax*(B_GABAB-A_GABAB) :compute time varying conductance as the difference of state variables B_GABAB and A_GABAB
+        g = g_GABAA + g_GABAB
+        i_GABAA = g_GABAA*(v-e_GABAA) :compute the GABAA driving force based on the time varying conductance, membrane potential, and GABAA reversal
+        i_GABAB = g_GABAB*(v-e_GABAB) :compute the GABAB driving force based on the time varying conductance, membrane potential, and GABAB reversal
+        i = i_GABAA + i_GABAB
+}
+
+PROCEDURE state() {
+        A_GABAA = A_GABAA*A_GABAA_step
+        B_GABAA = B_GABAA*B_GABAA_step
+        A_GABAB = A_GABAB*A_GABAB_step
+        B_GABAB = B_GABAB*B_GABAB_step
+}
+
+
+
+NET_RECEIVE (weight, weight_GABAA, weight_GABAB, Psurv, nc_type) {
+    : Psurv - survival probability of unrecovered state
+    : nc_type:
+    :   0 = presynaptic netcon
+    :   1 = spontmini netcon
+    :   2 = replay netcon
+
+    LOCAL result, ves, occu
+    weight_GABAA = weight
+    weight_GABAB = weight * GABAB_ratio
+
+    INITIAL {
+        if (nc_type == 0) {  :// presynaptic netcon
+    VERBATIM
+            // setup self events for delayed connections to change weights
+            IvocVect *vv_delay_times = *((IvocVect**)(&_p_delay_times));
+            IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+
+            if (vv_delay_times && vector_capacity(vv_delay_times)>=1) {
+                double* deltm_el = vector_vec(vv_delay_times);
+                int delay_times_idx;
+                next_delay = 0;
+                for (delay_times_idx = 0; delay_times_idx < vector_capacity(vv_delay_times); ++delay_times_idx) {
+                    double next_delay_t = deltm_el[delay_times_idx];
+    ENDVERBATIM
+                    net_send(next_delay_t, 1)
+    VERBATIM
+                }
+            }
+    ENDVERBATIM
+        }
+    }
+
+    if (flag == 1) {  :// self event to set next weight at
+        :UNITSOFF
+        :    printf( "gaba: self event at synapse %f time %g\n", synapseID, t)
+        :UNITSON
+    VERBATIM
+        // setup self events for delayed connections to change weights
+        IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+        if (vv_delay_weights && vector_capacity(vv_delay_weights)>=next_delay) {
+            double* weights_v = vector_vec(vv_delay_weights);
+            double next_delay_weight = weights_v[(int)next_delay];
+    ENDVERBATIM
+            weight = conductance * next_delay_weight
+            next_delay = next_delay + 1
+    VERBATIM
+        }
+        return;
+    ENDVERBATIM
+    }
+
+    : [flag == 0] Handle a spike which arrived
+    :UNITSOFF
+    :printf("[Syn %.0f] Received! (%f -> %f) with weight %g at time %g\n", synapseID, sgid, tgid, weight, t)
+    :UNITSON
+
+    : Do not perform any calculations if the synapse (netcon) is deactivated. This avoids drawing from
+    : random number stream. Also, disable in case of t < 0 (in case of ForwardSkip) which causes numerical
+    : instability if synapses are activated.
+    if ( weight <= 0 || t < 0 ) {
+    VERBATIM
+        return;
+    ENDVERBATIM
+    }
+
+    : calc u at event-
+    if (Fac > 0) {
+        u = u * exp(-(t - tsyn)/Fac)  :// update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
+    } else {
+        u = Use
+    }
+    if(Fac > 0){
+        u = u + Use*(1-u)  :// update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
+    }
+
+    : recovery
+    FROM counter = 0 TO (unoccupied - 1) {
+        : Iterate over all unoccupied sites and compute how many recover
+        Psurv = exp(-(t-tsyn)/Dep)
+        result = urand()
+        if (result>Psurv) {
+            occupied = occupied + 1     :// recover a previously unoccupied site
+            if ( verboseLevel > 0 ) {
+                UNITSOFF
+                printf("[Syn %.0f] Recovered! t = %g, Psurv = %g, urand = %g\n", synapseID, t, Psurv, result)
+                UNITSON
+            }
+        }
+    }
+
+    ves = 0                  :// Initialize the number of released vesicles to 0
+    occu = occupied          :// Make a copy, so we can update occupied in the loop
+    if (occu > 1 && minis_single_vesicle && nc_type == 1) {    : // if nc_type is spont_mini consider single vesicle
+        occu = 1
+    }
+    FROM counter = 0 TO (occu - 1) {
+        : iterate over all occupied sites and compute how many release
+        result = urand()
+        if (result < u) {
+            : release a single site!
+            occupied = occupied - 1  :// decrease the number of occupied sites by 1
+            ves = ves + 1            :// increase number of relesed vesicles by 1
+        }
+    }
+
+    : Update number of unoccupied sites
+    unoccupied = Nrrp - occupied
+
+    : Update tsyn
+    : tsyn knows about all spikes, not only those that released
+    : i.e. each spike can increase the u, regardless of recovered state.
+    :      and each spike trigger an evaluation of recovery
+    tsyn = t
+
+    if (ves > 0) { :no need to evaluate unless we have vesicle release
+        A_GABAA = A_GABAA + ves/Nrrp*weight_GABAA*factor_GABAA
+        B_GABAA = B_GABAA + ves/Nrrp*weight_GABAA*factor_GABAA
+        A_GABAB = A_GABAB + ves/Nrrp*weight_GABAB*factor_GABAB
+        B_GABAB = B_GABAB + ves/Nrrp*weight_GABAB*factor_GABAB
+
+        if ( verboseLevel > 0 ) {
+            UNITSOFF
+            printf("[Syn %.0f] Release! t = %g, vals: %g %g %g %g\n",
+                   synapseID, t, A_GABAA, weight_GABAA, factor_GABAA, weight)
+            UNITSON
+        }
+
+    } else {
+        : total release failure
+        if ( verboseLevel > 0 ) {
+            UNITSOFF
+            printf("[Syn %.0f] Failure! t = %g: urand = %g\n", synapseID, t, result)
+            UNITSON
+        }
+    }
+}
+
+
+PROCEDURE setRNG() {
+VERBATIM
+    #ifndef CORENEURON_BUILD
+    // For compatibility, allow for either MCellRan4 or Random123
+    // Distinguish by the arg types
+    // Object => MCellRan4, seeds (double) => Random123
+    usingR123 = 0;
+    if( ifarg(1) && hoc_is_double_arg(1) ) {
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        uint32_t a2 = 0;
+        uint32_t a3 = 0;
+
+        if (*pv) {
+            nrnran123_deletestream(*pv);
+            *pv = (nrnran123_State*)0;
+        }
+        if (ifarg(2)) {
+            a2 = (uint32_t)*getarg(2);
+        }
+        if (ifarg(3)) {
+            a3 = (uint32_t)*getarg(3);
+        }
+        *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3);
+        usingR123 = 1;
+    } else if( ifarg(1) ) {   // not a double, so assume hoc object type
+        void** pv = (void**)(&_p_rng);
+        *pv = nrn_random_arg(1);
+    } else {  // no arg, so clear pointer
+        void** pv = (void**)(&_p_rng);
+        *pv = (void*)0;
+    }
+    #endif
+ENDVERBATIM
+}
+
+
+PROCEDURE clearRNG() {
+VERBATIM
+    #ifndef CORENEURON_BUILD
+    if (usingR123) {
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        if (*pv) {
+            nrnran123_deletestream(*pv);
+            *pv = (nrnran123_State*)0;
+        }
+    } else {
+        void** pv = (void**)(&_p_rng);
+        if (*pv) {
+            *pv = (void*)0;
+        }
+    }
+    #endif
+ENDVERBATIM
+}
+
+
+FUNCTION urand() {
+VERBATIM
+    double value = 0.0;
+    if ( usingR123 ) {
+        value = nrnran123_dblpick((nrnran123_State*)_p_rng);
+    } else if (_p_rng) {
+        #ifndef CORENEURON_BUILD
+        value = nrn_random_pick(RANDCAST _p_rng);
+        #endif
+    } else {
+        // Note: prior versions used scop_random(1), but since we never use this model without configuring the rng.  Maybe should throw error?
+        value = 0.0;
+    }
+    _lurand = value;
+ENDVERBATIM
+}
+
+
+FUNCTION bbsavestate() {
+        bbsavestate = 0
+VERBATIM
+#ifndef CORENEURON_BUILD
+        /* first arg is direction (0 save, 1 restore), second is array*/
+        /* if first arg is -1, fill xdir with the size of the array */
+        double *xdir, *xval;
+#ifndef NRN_VERSION_GTEQ_8_2_0
+        double *hoc_pgetarg();
+        long nrn_get_random_sequence(void* r);
+        void nrn_set_random_sequence(void* r, int val);
+#endif
+        xdir = hoc_pgetarg(1);
+        xval = hoc_pgetarg(2);
+        if (_p_rng) {
+            // tell how many items need saving
+            if (*xdir == -1) {  // count items
+                if( usingR123 ) {
+                    *xdir = 2.0;
+                } else {
+                    *xdir = 1.0;
+                }
+                return 0.0;
+            } else if(*xdir ==0 ) {  // save
+                if( usingR123 ) {
+                    uint32_t seq;
+                    char which;
+                    nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which );
+                    xval[0] = (double) seq;
+                    xval[1] = (double) which;
+                } else {
+                    xval[0] = (double)nrn_get_random_sequence(RANDCAST _p_rng);
+                }
+            } else {  // restore
+                if( usingR123 ) {
+                    nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[0], (char)xval[1] );
+                } else {
+                    nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[0]));
+                }
+            }
+        }
+#endif
+ENDVERBATIM
+}
+
+FUNCTION toggleVerbose() {
+    verboseLevel = 1 - verboseLevel
+}
+
+
+VERBATIM
+static void bbcore_write(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
+  IvocVect *vv_delay_times = *((IvocVect**)(&_p_delay_times));
+  IvocVect *vv_delay_weights = *((IvocVect**)(&_p_delay_weights));
+
+  if (d) {
+    uint32_t* di = ((uint32_t*)d) + *d_offset;
+    nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+    nrnran123_getids3(*pv, di, di+1, di+2);
+
+    // write strem sequence
+    char which;
+    nrnran123_getseq(*pv, di+3, &which);
+    di[4] = (int)which;
+    //printf("ProbGABAAB_EMS bbcore_write %d %d %d\n", di[0], di[1], di[2]);
+
+  }
+  // reserve random123 parameters on serialization buffer
+  *d_offset += 5;
+
+  // serialize connection delay vectors
+  if (vv_delay_times && vv_delay_weights &&
+     (vector_capacity(vv_delay_times) >= 1) && (vector_capacity(vv_delay_weights) >= 1)) {
+    if (d) {
+      uint32_t* di = ((uint32_t*)d) + *d_offset;
+      // store vector sizes for deserialization
+      di[0] = vector_capacity(vv_delay_times);
+      di[1] = vector_capacity(vv_delay_weights);
+    }
+    if (x) {
+      double* delay_times_el = vector_vec(vv_delay_times);
+      double* delay_weights_el = vector_vec(vv_delay_weights);
+      double* x_i = x + *x_offset;
+      int delay_vecs_idx;
+      int x_idx = 0;
+      for(delay_vecs_idx = 0; delay_vecs_idx < vector_capacity(vv_delay_times); ++delay_vecs_idx) {
+         x_i[x_idx++] = delay_times_el[delay_vecs_idx];
+         x_i[x_idx++] = delay_weights_el[delay_vecs_idx];
+      }
+    }
+    // reserve space for connection delay data on serialization buffer
+    *x_offset += vector_capacity(vv_delay_times) + vector_capacity(vv_delay_weights);
+  } else {
+    if (d) {
+      uint32_t* di = ((uint32_t*)d) + *d_offset;
+      di[0] = 0;
+      di[1] = 0;
+    }
+
+  }
+  // reserve space for delay vectors (may be 0)
+  *d_offset += 2;
+
+}
+
+static void bbcore_read(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
+  // deserialize random123 data
+  uint32_t* di = ((uint32_t*)d) + *d_offset;
+  if (di[0] != 0 || di[1] != 0 || di[2] != 0) {
+      nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+#if !NRNBBCORE
+      if(*pv) {
+          nrnran123_deletestream(*pv);
+      }
+#endif
+      *pv = nrnran123_newstream3(di[0], di[1], di[2]);
+      // restore stream sequence
+      char which = (char)di[4];
+      nrnran123_setseq(*pv, di[3], which);
+  }
+  //printf("ProbAMPANMDA_EMS bbcore_read %d %d %d\n", di[0], di[1], di[2]);
+
+  int delay_times_sz = di[5];
+  int delay_weights_sz = di[6];
+  *d_offset += 7;
+
+  if ((delay_times_sz > 0) && (delay_weights_sz > 0)) {
+    double* x_i = x + *x_offset;
+
+    // allocate vectors
+    if (!_p_delay_times) {
+      _p_delay_times = (double*)vector_new1(delay_times_sz);
+    }
+    assert(delay_times_sz == vector_capacity((IvocVect*)_p_delay_times));
+    if (!_p_delay_weights) {
+      _p_delay_weights = (double*)vector_new1(delay_weights_sz);
+    }
+    assert(delay_weights_sz == vector_capacity((IvocVect*)_p_delay_weights));
+
+    double* delay_times_el = vector_vec((IvocVect*)_p_delay_times);
+    double* delay_weights_el = vector_vec((IvocVect*)_p_delay_weights);
+
+    // copy data
+    int x_idx;
+    int vec_idx = 0;
+    for(x_idx = 0; x_idx < delay_times_sz + delay_weights_sz; x_idx += 2) {
+      delay_times_el[vec_idx] = x_i[x_idx];
+      delay_weights_el[vec_idx++] = x_i[x_idx+1];
+    }
+    *x_offset += delay_times_sz + delay_weights_sz;
+
+  }
+}
+ENDVERBATIM
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SK_E2.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SK_E2.mod
new file mode 100755
index 00000000..ae552ab2
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SK_E2.mod
@@ -0,0 +1,57 @@
+: SK-type calcium-activated potassium current
+: Reference : Kohler et al. 1996
+: LJP: OK, correction not necessary, no voltage values
+
+NEURON {
+       SUFFIX SK_E2
+       USEION k READ ek WRITE ik
+       USEION ca READ cai
+       RANGE gSK_E2bar, gSK_E2, ik, zTau
+}
+
+UNITS {
+      (mV) = (millivolt)
+      (mA) = (milliamp)
+      (mM) = (milli/liter)
+}
+
+PARAMETER {
+          v            (mV)
+          gSK_E2bar = .000001 (mho/cm2)
+          zTau = 1              (ms)
+          ek           (mV)
+          cai          (mM)
+}
+
+ASSIGNED {
+         zInf
+         ik            (mA/cm2)
+         gSK_E2	       (S/cm2)
+}
+
+STATE {
+      z   FROM 0 TO 1
+}
+
+BREAKPOINT {
+           SOLVE states METHOD cnexp
+           gSK_E2  = gSK_E2bar * z
+           ik   =  gSK_E2 * (v - ek)
+}
+
+DERIVATIVE states {
+        rates(cai)
+        z' = (zInf - z) / zTau
+}
+
+PROCEDURE rates(ca(mM)) {
+          if(ca < 1e-7){
+	              ca = ca + 1e-07
+          }
+          zInf = 1/(1 + (0.00043 / ca)^4.8)
+}
+
+INITIAL {
+        rates(cai)
+        z = zInf
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SKv3_1.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SKv3_1.mod
new file mode 100755
index 00000000..8bd99d02
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/SKv3_1.mod
@@ -0,0 +1,56 @@
+:Comment :
+:Reference : :	Rettig et.al (1992) EMBO J 11, no. 7: 2473-86.
+:								Methods: Grupe et al. (1990) EMBO J 9, 1749-1756.
+: LJP: OK, no LJP, "Patch pipetes were filed with the normal bathing solution in al experiments.""
+
+NEURON	{
+	SUFFIX SKv3_1
+	USEION k READ ek WRITE ik
+	RANGE gSKv3_1bar, gSKv3_1, ik
+}
+
+UNITS	{
+	(S) = (siemens)
+	(mV) = (millivolt)
+	(mA) = (milliamp)
+}
+
+PARAMETER	{
+	gSKv3_1bar = 0.00001 (S/cm2)
+}
+
+ASSIGNED	{
+	v	(mV)
+	ek	(mV)
+	ik	(mA/cm2)
+	gSKv3_1	(S/cm2)
+	mInf
+	mTau
+}
+
+STATE	{
+	m
+}
+
+BREAKPOINT	{
+	SOLVE states METHOD cnexp
+	gSKv3_1 = gSKv3_1bar*m
+	ik = gSKv3_1*(v-ek)
+}
+
+DERIVATIVE states	{
+	rates()
+	m' = (mInf-m)/mTau
+}
+
+INITIAL{
+	rates()
+	m = mInf
+}
+
+PROCEDURE rates(){
+	UNITSOFF
+		mInf =  1/(1+exp(((v -(18.700))/(-9.700))))
+		mTau =  0.2*20.000/(1+exp(((v -(-46.560))/(-44.140))))
+	UNITSON
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/StochKv3.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/StochKv3.mod
new file mode 100755
index 00000000..f07ac8ea
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/StochKv3.mod
@@ -0,0 +1,545 @@
+TITLE StochKv3.mod
+
+COMMENT
+----------------------------------------------------------------
+Stochastic inactivating channel using values reported in Mendonca et al. 2016.
+(Modified from https://senselab.med.yale.edu/modeldb/showmodel.cshtml?model=125385&file=/Sbpap_code/mod/skaprox.mod)
+
+The model keeps track on the number of channels in each state, and
+uses a binomial distribution to update these number.
+
+Jan 1999, Mickey London, Hebrew University, mikilon@lobster.ls.huji.ac.il
+        Peter N. Steinmetz, Caltech, peter@klab.caltech.edu
+14 Sep 99 PNS. Added deterministic flag.
+19 May 2002 Kamran Diba.  Changed gamma and deterministic from GLOBAL to RANGE.
+23 Nov 2011 Werner Van Geit @ BBP. Changed the file so that it can use the neuron random number generator. Tuned voltage dependence
+16 Mar 2016 James G King @ BBP.  Incorporate modifications suggested by Michael Hines to improve stiching to deterministic mode, thread safety, and using Random123
+26 Sep 2016 Christian Roessert @ BBP. Adding inactivation, changing dynamics to values reported in Mendonca et al. 2016
+: LJP: OK, whole-cell patch, corrected by 10 mV (Mendonca et al. 2016)
+
+----------------------------------------------------------------
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+    SUFFIX StochKv3
+    THREADSAFE
+    USEION k READ ek WRITE ik
+    RANGE N, eta, gk, gamma, deterministic, gkbar, ik
+    RANGE N0, N1, n0_n1, n1_n0
+    RANGE ninf, linf, ltau, ntau, an, bn, al, bl
+    RANGE P_an, P_bn, P_al, P_bl
+    GLOBAL vmin, vmax
+    BBCOREPOINTER rng
+    :POINTER rng
+}
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (S) = (siemens)
+    (um) = (micron)
+}
+
+PARAMETER {
+    v           (mV)
+    dt      (ms)
+    area    (um2)
+
+    gamma  =  50      (pS)
+    eta              (1/um2)
+    gkbar = .01      (S/cm2)
+
+    deterministic = 0   : if non-zero, will use deterministic version
+    vmin = -120 (mV)    : range to construct tables for
+    vmax = 100  (mV)
+}
+
+ASSIGNED {
+    an      (/ms)
+    bn      (/ms)
+    al      (/ms)
+    bl      (/ms)
+    ik      (mA/cm2)
+    gk      (S/cm2)
+    ek      (mV)
+    ninf        : steady-state value
+    ntau (ms)   : time constant for relaxation
+    linf        : steady-state value
+    ltau (ms)   : time constant for relaxation
+
+    N
+    scale_dens (pS/um2)
+    P_an     : probability of one channel making alpha n transition
+    P_bn     : probability of one channel making beta n transition
+    P_al     : probability of one channel making alpha l transition
+    P_bl     : probability of one channel making beta l transition
+
+    rng
+    usingR123
+}
+
+
+STATE {
+    n l  : state variable of deterministic description
+}
+ASSIGNED {
+    N0L0 N1L0 N0L1 N1L1     : N states populations (These currently will not be saved via the bbsavestate functionality.  Would need to be STATE again)
+    n0l0_n1l0 n0l0_n0l1     : number of channels moving from one state to the other
+    n1l0_n1l1 n1l0_n0l0
+    n0l1_n1l1 n0l1_n0l0
+    n1l1_n0l1 n1l1_n1l0
+}
+
+COMMENT
+The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1
+for comparison with Pr to decide whether to activate the synapse or not
+ENDCOMMENT
+
+VERBATIM
+#ifndef NRN_VERSION_GTEQ_8_2_0
+#include "nrnran123.h"
+extern int cvode_active_;
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+
+#ifndef CORENEURON_BUILD
+double nrn_random_pick(void* r);
+void* nrn_random_arg(int argpos);
+#endif
+#define RANDCAST
+#else
+#define RANDCAST (Rand*)
+#endif
+
+ENDVERBATIM
+: ----------------------------------------------------------------
+: initialization
+INITIAL {
+    VERBATIM
+    if (cvode_active_ && !deterministic) {
+        hoc_execerror("StochKv2 with deterministic=0", "cannot be used with cvode");
+    }
+
+    if( usingR123 ) {
+        nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0);
+    }
+    ENDVERBATIM
+
+    eta = (gkbar / gamma) * (10000)
+    trates(v)
+    n=ninf
+    l=linf
+    scale_dens = gamma/area
+    N = floor(eta*area + 0.5)
+
+    N1L1 = n*l*N
+    N1L0 = n*(1-l)*N
+    N0L1 = (1-n)*l*N
+    if( !deterministic) {
+        N1L1 = floor(N1L1 + 0.5)
+        N1L0 = floor(N1L0 + 0.5)
+        N0L1 = floor(N0L1 + 0.5)
+    }
+    N0L0 = N - N1L1 - N1L0 - N0L1  : put rest into non-conducting state
+
+    n0l0_n1l0 = 0
+    n0l0_n0l1 = 0
+    n1l0_n1l1 = 0
+    n1l0_n0l0 = 0
+    n0l1_n1l1 = 0
+    n0l1_n0l0 = 0
+    n1l1_n0l1 = 0
+    n1l1_n1l0 = 0
+}
+
+: ----------------------------------------------------------------
+: Breakpoint for each integration step
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+
+  gk = (strap(N1L1) * scale_dens) * (0.0001)
+
+  ik = gk * (v - ek)
+}
+
+
+: ----------------------------------------------------------------
+: states - updates number of channels in each state
+DERIVATIVE states {
+
+    trates(v)
+
+    l' = al - (al + bl)*l
+    n' = an - (an + bn)*n
+
+    if (deterministic || dt > 1) { : ForwardSkip is also deterministic
+
+      N1L1 = n*l*N
+      N1L0 = n*(1-l)*N
+      N0L1 = (1-n)*l*N
+
+    }else{
+
+      : ensure that N0 is an integer for when transitioning from deterministic mode to stochastic mode
+      N1L1 = floor(N1L1 + 0.5)
+      N1L0 = floor(N1L0 + 0.5)
+      N0L1 = floor(N0L1 + 0.5)
+      N0L0 = N - N1L1 - N1L0 - N0L1
+
+      P_an = strap(an*dt)
+      P_bn = strap(bn*dt)
+      : check that will represent probabilities when used
+      ChkProb(P_an)
+      ChkProb(P_bn)
+
+      : n gate transitions
+      n0l0_n1l0 = BnlDev(P_an, N0L0)
+      n0l1_n1l1 = BnlDev(P_an, N0L1)
+      n1l1_n0l1 = BnlDev(P_bn, N1L1)
+      n1l0_n0l0 = BnlDev(P_bn, N1L0)
+
+      : move the channels
+      N0L0 = strap(N0L0 - n0l0_n1l0 + n1l0_n0l0)
+      N1L0 = strap(N1L0 - n1l0_n0l0 + n0l0_n1l0)
+      N0L1 = strap(N0L1 - n0l1_n1l1 + n1l1_n0l1)
+      N1L1 = strap(N1L1 - n1l1_n0l1 + n0l1_n1l1)
+
+      : probabilities of making l gate transitions
+      P_al = strap(al*dt)
+      P_bl  = strap(bl*dt)
+      : check that will represent probabilities when used
+      ChkProb(P_al)
+      ChkProb(P_bl)
+
+      : number making l gate transitions
+      n0l0_n0l1 = BnlDev(P_al,N0L0-n0l0_n1l0)
+      n1l0_n1l1 = BnlDev(P_al,N1L0-n1l0_n0l0)
+      n0l1_n0l0 = BnlDev(P_bl,N0L1-n0l1_n1l1)
+      n1l1_n1l0 = BnlDev(P_bl,N1L1-n1l1_n0l1)
+
+      : move the channels
+      N0L0 = strap(N0L0 - n0l0_n0l1  + n0l1_n0l0)
+      N1L0 = strap(N1L0 - n1l0_n1l1  + n1l1_n1l0)
+      N0L1 = strap(N0L1 - n0l1_n0l0  + n0l0_n0l1)
+      N1L1 = strap(N1L1 - n1l1_n1l0  + n1l0_n1l1)
+
+    }
+
+    N0L0 = N - N1L1 - N1L0 - N0L1  : put rest into non-conducting state
+}
+
+: ----------------------------------------------------------------
+: trates - compute rates, using table if possible
+PROCEDURE trates(v (mV)) {
+    TABLE ntau,ltau,ninf,linf,al,bl,an,bn
+    DEPEND dt
+    FROM vmin TO vmax WITH 199
+
+    v = v + 10
+    linf = 1/(1+exp((-30(mV)-v)/10(mV)))
+    ltau = 0.346(ms)*exp(-v/(18.272(mV)))+2.09(ms)
+
+    ninf = 1/(1+exp(0.0878(1/mV)*(v+55.1(mV))))
+    ntau = 2.1(ms)*exp(-v/21.2(mV))+4.627(ms)
+    v = v - 10
+
+    al = linf/ltau
+    bl = 1/ltau - al
+    an = ninf/ntau
+    bn = 1/ntau - an
+}
+
+: ----------------------------------------------------------------
+: sign trap - trap for negative values and replace with zero
+FUNCTION strap(x) {
+    if (x < 0) {
+        strap = 0
+: This function gets executed in the DERIVATIVE and BREAKPOINT blocks
+: which should get vectorized and fprintf doesn't allow this
+: Also Intel classic compilers generate invalid code when #pragma omp simd is used
+:VERBATIM
+:        fprintf (stderr,"skv.mod:strap: negative state");
+:ENDVERBATIM
+    } else {
+        strap = x
+    }
+}
+
+: ----------------------------------------------------------------
+: ChkProb - Check that number represents a probability
+PROCEDURE ChkProb(p) {
+
+  if (p < 0.0 || p > 1.0) {
+    VERBATIM
+    fprintf(stderr, "StochKv2.mod:ChkProb: argument not a probability.\n");
+    ENDVERBATIM
+  }
+
+}
+
+PROCEDURE setRNG() {
+
+VERBATIM
+    // For compatibility, allow for either MCellRan4 or Random123.  Distinguish by the arg types
+    // Object => MCellRan4, seeds (double) => Random123
+#ifndef CORENEURON_BUILD
+    usingR123 = 0;
+    if( ifarg(1) && hoc_is_double_arg(1) ) {
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        uint32_t a2 = 0;
+        uint32_t a3 = 0;
+
+        if (*pv) {
+            nrnran123_deletestream(*pv);
+            *pv = (nrnran123_State*)0;
+        }
+        if (ifarg(2)) {
+            a2 = (uint32_t)*getarg(2);
+        }
+        if (ifarg(3)) {
+            a3 = (uint32_t)*getarg(3);
+        }
+        *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3);
+        usingR123 = 1;
+    } else if( ifarg(1) ) {
+        void** pv = (void**)(&_p_rng);
+        *pv = nrn_random_arg(1);
+    } else {
+        void** pv = (void**)(&_p_rng);
+        *pv = (void*)0;
+    }
+#endif
+ENDVERBATIM
+}
+
+FUNCTION urand() {
+
+VERBATIM
+    double value;
+    if( usingR123 ) {
+        value = nrnran123_dblpick((nrnran123_State*)_p_rng);
+    } else if (_p_rng) {
+#ifndef CORENEURON_BUILD
+        value = nrn_random_pick(RANDCAST _p_rng);
+#endif
+    } else {
+        value = 0.5;
+    }
+    _lurand = value;
+ENDVERBATIM
+}
+
+VERBATIM
+static void bbcore_write(double* x, int* d, int* xx, int* offset, _threadargsproto_) {
+    if (d) {
+        uint32_t* di = ((uint32_t*)d) + *offset;
+      // temporary just enough to see how much space is being used
+      if (!_p_rng) {
+        di[0] = 0; di[1] = 0, di[2] = 0;
+      }else{
+        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+        nrnran123_getids3(*pv, di, di+1, di+2);
+        // write stream sequence
+        char which;
+        nrnran123_getseq(*pv, di+3, &which);
+        di[4] = (int)which;
+      }
+      //printf("StochKv3.mod %p: bbcore_write offset=%d %d %d\n", _p, *offset, d?di[0]:-1, d?di[1]:-1);
+    }
+    *offset += 5;
+}
+static void bbcore_read(double* x, int* d, int* xx, int* offset, _threadargsproto_) {
+    uint32_t* di = ((uint32_t*)d) + *offset;
+        if (di[0] != 0 || di[1] != 0|| di[2] != 0)
+        {
+      nrnran123_State** pv = (nrnran123_State**)(&_p_rng);
+#if !NRNBBCORE
+      if(*pv) {
+          nrnran123_deletestream(*pv);
+      }
+#endif
+      *pv = nrnran123_newstream3(di[0], di[1], di[2]);
+      nrnran123_setseq(*pv, di[3], (char)di[4]);
+        }
+      //printf("StochKv3.mod %p: bbcore_read offset=%d %d %d\n", _p, *offset, di[0], di[1]);
+    *offset += 5;
+}
+ENDVERBATIM
+
+: Returns random numbers drawn from a binomial distribution
+FUNCTION brand(P, N) {
+
+VERBATIM
+        /*
+        :Supports separate independent but reproducible streams for
+        : each instance. However, the corresponding hoc Random
+        : distribution MUST be set to Random.uniform(0,1)
+        */
+
+        // Should probably be optimized
+        double value = 0.0;
+        int i;
+        for (i = 0; i < _lN; i++) {
+           if (urand(_threadargs_) < _lP) {
+              value = value + 1;
+           }
+        }
+        return(value);
+
+ENDVERBATIM
+
+        brand = value
+}
+
+VERBATIM
+#define        PI 3.141592654
+#define        r_ia     16807
+#define        r_im     2147483647
+#define        r_am     (1.0/r_im)
+#define        r_iq     127773
+#define        r_ir     2836
+#define        r_ntab   32
+#define        r_ndiv   (1+(r_im-1)/r_ntab)
+#define        r_eps    1.2e-7
+#define        r_rnmx   (1.0-r_eps)
+ENDVERBATIM
+
+VERBATIM
+/* ---------------------------------------------------------------- */
+/* gammln - compute natural log of gamma function of xx */
+static double
+gammln(double xx)
+{
+    double x,tmp,ser;
+    static double cof[6]={76.18009173,-86.50532033,24.01409822,
+        -1.231739516,0.120858003e-2,-0.536382e-5};
+    int j;
+    x=xx-1.0;
+    tmp=x+5.5;
+    tmp -= (x+0.5)*log(tmp);
+    ser=1.0;
+    for (j=0;j<=5;j++) {
+        x += 1.0;
+        ser += cof[j]/x;
+    }
+    return -tmp+log(2.50662827465*ser);
+}
+ENDVERBATIM
+
+
+: ----------------------------------------------------------------
+: BnlDev - draw a uniform deviate from the generator
+FUNCTION BnlDev (ppr, nnr) {
+
+VERBATIM
+        int j;
+        double am,em,g,angle,p,bnl,sq,bt,y;
+        double pc,plog,pclog,en,oldg;
+
+        /* prepare to always ignore errors within this routine */
+
+        p=(_lppr <= 0.5 ? _lppr : 1.0-_lppr);
+        am=_lnnr*p;
+        if (_lnnr < 25) {
+            bnl=0.0;
+            for (j=1;j<=_lnnr;j++)
+                if (urand(_threadargs_) < p) bnl += 1.0;
+        }
+        else if (am < 1.0) {
+            g=exp(-am);
+            bt=1.0;
+            for (j=0;j<=_lnnr;j++) {
+                bt *= urand(_threadargs_);
+                if (bt < g) break;
+            }
+            bnl=(j <= _lnnr ? j : _lnnr);
+        }
+        else {
+            {
+                en=_lnnr;
+                oldg=gammln(en+1.0);
+            }
+            {
+                pc=1.0-p;
+                plog=log(p);
+                pclog=log(pc);
+            }
+            sq=sqrt(2.0*am*pc);
+            do {
+                do {
+                    angle=PI*urand(_threadargs_);
+                    angle=PI*urand(_threadargs_);
+                    y=tan(angle);
+                    em=sq*y+am;
+                } while (em < 0.0 || em >= (en+1.0));
+                em=floor(em);
+                    bt=1.2*sq*(1.0+y*y)*exp(oldg-gammln(em+1.0) -
+                    gammln(en-em+1.0)+em*plog+(en-em)*pclog);
+            } while (urand(_threadargs_) > bt);
+            bnl=em;
+        }
+        if (p != _lppr) bnl=_lnnr-bnl;
+
+        /* recover error if changed during this routine, thus ignoring
+            any errors during this routine */
+
+
+        return bnl;
+
+    ENDVERBATIM
+    BnlDev = bnl
+}
+
+FUNCTION bbsavestate() {
+        bbsavestate = 0
+VERBATIM
+ #ifndef CORENEURON_BUILD
+        // TODO: since N0,N1 are no longer state variables, they will need to be written using this callback
+        //  provided that it is the version that supports multivalue writing
+        /* first arg is direction (-1 get info, 0 save, 1 restore), second is value*/
+        double *xdir, *xval;
+        #ifndef NRN_VERSION_GTEQ_8_2_0
+        double *hoc_pgetarg();
+        long nrn_get_random_sequence(void* r);
+        void nrn_set_random_sequence(void* r, int val);
+        #endif
+        xdir = hoc_pgetarg(1);
+        xval = hoc_pgetarg(2);
+        if (_p_rng) {
+                // tell how many items need saving
+                if (*xdir == -1.) {
+                    if( usingR123 ) {
+                        *xdir = 2.0;
+                    } else {
+                        *xdir = 1.0;
+                    }
+                    return 0.0;
+                }
+                else if (*xdir == 0.) {
+                    if( usingR123 ) {
+                        uint32_t seq;
+                        char which;
+                        nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which );
+                        xval[0] = (double) seq;
+                        xval[1] = (double) which;
+                    } else {
+                        xval[0] = (double)nrn_get_random_sequence(RANDCAST _p_rng);
+                    }
+                } else{
+                    if( usingR123 ) {
+                        nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[0], (char)xval[1] );
+                    } else {
+                        nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[0]));
+                    }
+                }
+        }
+
+        // TODO: check for random123 and get the seq values
+#endif
+ENDVERBATIM
+}
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/TTXDynamicsSwitch.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/TTXDynamicsSwitch.mod
new file mode 100755
index 00000000..c74d1167
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/TTXDynamicsSwitch.mod
@@ -0,0 +1,49 @@
+: Simple switching TTX dynamics
+: Outside TTX concentration level stays fixed at ttxo_level, which can be
+: set as a range variable
+: Implemented by Werner Van Geit @ BlueBrain Project, Jan 2015
+
+NEURON	{
+	SUFFIX TTXDynamicsSwitch
+	USEION ttx WRITE ttxo, ttxi VALENCE 1
+    RANGE ttxo_level
+}
+
+PARAMETER	{
+    : 1e-12 represent 'no TTX', using 0.0 could generate problems during the 
+    : possible calculation of ettx 
+    ttxo_level = 1e-12 (mM)
+
+    : Check at the end of the mod file for the reasoning behind this sentinel
+    : value
+    ttxi_sentinel = 0.015625 (mM) : 1.0/64.0
+}
+
+ASSIGNED {
+    ttxo (mM)
+    ttxi (mM)
+}
+
+BREAKPOINT	{
+    ttxo = ttxo_level
+}
+
+INITIAL {
+    ttxo = ttxo_level
+    ttxi = ttxi_sentinel
+}
+
+: WVG @ BBP, Jan 2015
+: The internal ttx concentration is a sentinel value, this value should
+: not be used for any calculation
+: The reason it is here is that Neuron's default value for a concentration
+: is 1.0. In case a sodium channel uses the TTX concentration to 
+: enable/disable itself, it shouldn't use the default value. It should only
+: use the ttxo value when it has been initialised by this TTXDynamicsSwitch
+: mechanism.
+: (or any other value that manages the outside ttx concentration)
+: The channel should read the ttxi value, and check for this sentinel value
+: If it matches, it means this mechanism is control the ttxo concentration
+: otherwise ttxo should be ignored
+: Chose 1.0/64.0 as sentinel because it can be exactly represented in binary
+: floating-point representation
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/vecevent.mod b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/vecevent.mod
new file mode 100644
index 00000000..2947a2d7
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/mod/vecevent.mod
@@ -0,0 +1,163 @@
+:  Vector stream of events, copied from nrn/share/examples/nrniv/netcon/vecevent.mod
+
+COMMENT
+A VecStim is an artificial spiking cell that generates
+events at times that are specified in a Vector.
+
+HOC Example:
+
+// assumes spt is a Vector whose elements are all > 0
+// and are sorted in monotonically increasing order
+objref vs
+vs = new VecStim()
+vs.play(spt)
+// now launch a simulation, and vs will produce spike events
+// at the times contained in spt
+
+Python Example:
+
+from neuron import h
+spt = h.Vector(10).indgen(1, 0.2)
+vs = h.VecStim()
+vs.play(spt)
+
+def pr():
+  print (h.t)
+
+nc = h.NetCon(vs, None)
+nc.record(pr)
+
+cvode = h.CVode()
+h.finitialize()
+cvode.solve(20)
+
+ENDCOMMENT
+
+NEURON {
+	THREADSAFE
+	ARTIFICIAL_CELL VecStim
+	BBCOREPOINTER ptr
+}
+
+ASSIGNED {
+	index
+	etime (ms)
+	ptr
+}
+
+
+INITIAL {
+	index = 0
+	element()
+	if (index > 0) {
+		net_send(etime - t, 1)
+	}
+}
+
+NET_RECEIVE (w) {
+	if (flag == 1) {
+		net_event(t)
+		element()
+		if (index > 0) {
+			net_send(etime - t, 1)
+		}
+	}
+}
+
+DESTRUCTOR {
+VERBATIM
+#if !NRNBBCORE
+	void* vv = (void*)(_p_ptr);  
+        if (vv) {
+		hoc_obj_unref(*vector_pobj(vv));
+	}
+#endif
+ENDVERBATIM
+}
+
+PROCEDURE element() {
+VERBATIM	
+  { void* vv; int i, size; double* px;
+	i = (int)index;
+	if (i >= 0) {
+		vv = (void*)(_p_ptr);
+		if (vv) {
+			size = vector_capacity(vv);
+			px = vector_vec(vv);
+			if (i < size) {
+				etime = px[i];
+				index += 1.;
+			}else{
+				index = -1.;
+			}
+		}else{
+			index = -1.;
+		}
+	}
+  }
+ENDVERBATIM
+}
+
+PROCEDURE play() {
+VERBATIM
+#if !NRNBBCORE
+  {
+	void** pv;
+	void* ptmp = NULL;
+	if (ifarg(1)) {
+		ptmp = vector_arg(1);
+		hoc_obj_ref(*vector_pobj(ptmp));
+	}
+	pv = (void**)(&_p_ptr);
+	if (*pv) {
+		hoc_obj_unref(*vector_pobj(*pv));
+	}
+	*pv = ptmp;
+  }
+#endif
+ENDVERBATIM
+}
+
+VERBATIM
+static void bbcore_write(double* xarray, int* iarray, int* xoffset, int* ioffset, _threadargsproto_) {
+  int i, dsize, *ia;
+  double *xa, *dv;
+  dsize = 0;
+  if (_p_ptr) {
+    dsize = vector_capacity(_p_ptr);
+  }
+  if (iarray) {
+    void* vec = _p_ptr;
+    ia = iarray + *ioffset;
+    xa = xarray + *xoffset;
+    ia[0] = dsize;
+    if (dsize) {
+      dv = vector_vec(vec);
+      for (i = 0; i < dsize; ++i) {
+         xa[i] = dv[i];
+      }
+    }
+  }
+  *ioffset += 1;
+  *xoffset += dsize;
+}
+
+static void bbcore_read(double* xarray, int* iarray, int* xoffset, int* ioffset, _threadargsproto_) {
+  int dsize, i, *ia;
+  double *xa, *dv;
+  xa = xarray + *xoffset;
+  ia = iarray + *ioffset;
+  dsize = ia[0];
+  if (!_p_ptr) {
+    _p_ptr = vector_new1(dsize);
+  }
+  assert(dsize == vector_capacity(_p_ptr));
+  dv = vector_vec(_p_ptr);
+  for (i = 0; i < dsize; ++i) {
+    dv[i] = xa[i];
+  }
+  *xoffset += dsize;
+  *ioffset += 1;
+}
+
+ENDVERBATIM
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/asc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/asc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc
new file mode 100644
index 00000000..7b684b32
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/asc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc
@@ -0,0 +1,15797 @@
+("CellBody"
+  (Color Red)
+  (CellBody)
+  (6.756666660 -5.596673965 -2.753613949 0.000000000)
+  (5.186659336 -7.026666641 -1.303662896 0.000000000)
+  (3.506666660 -7.956659317 0.066332266 0.000000000)
+  (1.766676426 -9.186670303 1.556383491 0.000000000)
+  (0.116652057 -10.256677628 2.956346989 0.000000000)
+  (-1.073319912 -11.156671524 3.996386051 0.000000000)
+  (-2.793321133 -11.216669083 5.136339664 0.000000000)
+  (-4.443345547 -10.686670303 6.056383610 0.000000000)
+  (-6.433335781 -9.026666641 6.856371403 0.000000000)
+  (-8.083329201 -7.566675186 7.496386051 0.000000000)
+  (-9.293335915 -5.496667862 7.676378727 0.000000000)
+  (-9.703339577 -3.336664438 7.306383610 0.000000000)
+  (-9.653336525 -1.676660657 6.786364079 0.000000000)
+  (-8.603333473 0.293340504 5.526354313 0.000000000)
+  (-7.623338223 2.543340445 4.216356754 0.000000000)
+  (-6.833329678 3.913335562 3.296373844 0.000000000)
+  (-5.703340054 5.813329697 2.006334782 0.000000000)
+  (-4.313340664 7.523351669 0.586351812 0.000000000)
+  (-2.773332119 9.163335800 -0.893628657 0.000000000)
+  (-1.893327236 10.693334579 -1.923658013 0.000000000)
+  (-0.703324735 11.593328476 -2.963635921 0.000000000)
+  (0.736677706 11.843328476 -3.973645687 0.000000000)
+  (2.386671543 11.973333359 -5.093640804 0.000000000)
+  (3.626661777 11.283330917 -5.703626156 0.000000000)
+  (5.156660557 8.853338242 -5.973645687 0.000000000)
+  (6.656660557 6.573339462 -6.273633480 0.000000000)
+  (7.556654453 5.353338242 -6.503613949 0.000000000)
+  (7.826673985 3.693334341 -6.183667660 0.000000000)
+  (7.916670322 1.663335681 -5.643628597 0.000000000)
+  (8.136672020 -0.186670482 -5.253613949 0.000000000)
+  (8.076674461 -2.796655893 -4.443616390 0.000000000)
+  (7.356672764 -4.536676407 -3.463635921 0.000000000)
+  (7.156660557 -5.066675186 -3.173657894 0.000000000)
+)
+
+( (Color Cyan)
+  (Axon)
+  (-4.053330898 -9.116840363 5.476366520 2.359999895)
+  (-3.374589920 -13.104918480 7.070360184 2.359999895)
+  (-3.253740549 -14.261247635 7.368614674 2.359999895)
+  (-3.073751211 -17.629720688 7.595680237 2.359999895)
+  (-3.052903891 -18.701614380 7.491483212 2.359999895)
+  (-3.059401989 -19.512538910 7.307871819 2.359999895)
+  (-3.062656641 -21.279109955 6.748314381 2.279999971)
+  (-3.077791691 -23.076576233 5.884872913 2.210000038)
+  (-3.002447605 -25.234798431 4.624418736 1.549999952)
+  (-2.820800304 -27.110391617 3.364657164 1.549999952)
+  (-2.564836264 -28.720222473 2.280065775 1.470000029)
+  (-2.113248348 -30.789505005 1.019327044 1.549999952)
+  (-1.774627328 -31.996749878 0.378401518 1.549999952)
+  (-1.337312937 -33.396404266 -0.227511168 1.549999952)
+  (-0.959475219 -34.626815796 -0.670673072 1.769999981)
+  (-0.634851575 -35.881755829 -1.046917915 1.769999981)
+  (-0.304206550 -37.745342255 -1.505425692 1.399999976)
+  (-0.221714914 -39.226528168 -1.844122648 1.250000000)
+  (-0.278632462 -40.903602600 -2.204269886 1.620000005)
+  (-0.414358854 -42.406272888 -2.500162125 1.990000010)
+  (-0.599109709 -43.811965942 -2.845025539 2.140000105)
+  (-0.748467445 -44.704719543 -3.113039017 1.990000010)
+  (-1.195088506 -46.872875214 -3.836452484 1.250000000)
+  (-1.458719015 -47.981147766 -4.170539379 1.110000014)
+  (-1.765674114 -49.175060272 -4.548319340 1.179999948)
+  (-2.292579412 -51.289440155 -5.094665527 1.110000014)
+  (-2.710265636 -53.235881805 -5.514403343 1.179999948)
+  (-3.030727863 -55.123374939 -5.838611603 1.330000043)
+  (-3.219191074 -56.699890137 -6.049961090 1.330000043)
+  (-3.329719067 -58.366931915 -6.249066830 1.179999948)
+  (-3.397731066 -61.126701355 -6.478947639 1.029999971)
+  (-3.391671658 -63.141067505 -6.563755989 1.250000000)
+  (-3.398003817 -64.388214111 -6.611573219 1.250000000)
+  (-3.484790802 -66.115829468 -6.722644329 1.399999976)
+  (-3.655171394 -67.263557434 -6.829598904 1.399999976)
+  (-4.010252476 -68.526756287 -6.984999657 1.250000000)
+  (-4.567257881 -69.846168518 -7.240869045 1.179999948)
+  (-5.373519421 -71.296318054 -7.506228924 1.330000043)
+  (-5.974987984 -72.242378235 -7.643045425 1.399999976)
+  (-6.846679688 -73.489913940 -7.717358589 1.399999976)
+  (-7.774118423 -74.770149231 -7.685330391 1.179999948)
+  (-8.326965332 -75.611465454 -7.613328457 1.179999948)
+  (-8.651199341 -76.247833252 -7.514973164 1.179999948)
+  (-8.856595993 -76.914428711 -7.378986835 1.179999948)
+  (-8.917907715 -78.005851746 -7.139876366 1.179999948)
+  (-8.779551506 -78.857757568 -6.956852913 1.179999948)
+  (-8.165764809 -80.886528015 -6.557646275 1.029999971)
+  (-7.630985260 -82.390609741 -6.280062675 1.029999971)
+  (-7.107389927 -83.770721436 -6.086539745 1.179999948)
+  (-6.301667690 -85.915710449 -5.891699791 1.330000043)
+  (-5.692833900 -87.645370483 -5.770153522 1.399999976)
+  (-5.215092659 -88.975753784 -5.683917046 1.470000029)
+  (-4.521416664 -91.063560486 -5.559239864 1.470000029)
+  (-4.187116146 -92.322525024 -5.504452229 1.330000043)
+  (-3.946187496 -93.526756287 -5.459670544 1.620000005)
+  (-3.770617008 -95.375274658 -5.405253887 1.620000005)
+  (
+    (-3.770617008 -95.375274658 -5.405253887 1.110000014)
+    (-4.942147255 -99.187225342 -6.043909073 1.110000014)
+    (-5.892145157 -101.844093323 -6.421503544 1.110000014)
+    (-6.118875027 -102.500083923 -6.499510765 1.110000014)
+    (-7.187160969 -105.983070374 -6.862864971 1.029999971)
+    (-7.741626740 -108.335166931 -7.079350471 1.029999971)
+    (-8.368066788 -111.745567322 -7.366420269 1.029999971)
+    (-8.611768723 -113.403366089 -7.439717293 1.029999971)
+    (-8.980390549 -116.379585266 -7.571156502 1.029999971)
+    (-9.185726166 -118.876029968 -7.672949791 1.029999971)
+    (-9.206117630 -122.750892639 -7.812004566 0.959999979)
+    (-9.023235321 -124.827499390 -7.897230148 0.959999979)
+    (-8.935536385 -125.263046265 -7.914501190 0.959999979)
+    (
+      (-8.935536385 -125.263046265 -7.914501190 0.959999979)
+      (-8.897560120 -126.870735168 -8.008177757 0.959999979)
+      (-8.895828247 -129.090118408 -8.237664223 0.959999979)
+      (-8.932153702 -130.855178833 -8.455522537 0.959999979)
+      (-9.002612114 -133.718505859 -8.830714226 0.959999979)
+      (-9.147162437 -137.310256958 -9.314194679 0.959999979)
+      (-9.242323875 -139.272842407 -9.581144333 0.810000002)
+      (-9.317924500 -140.817840576 -9.811566353 0.810000002)
+      (-9.348378181 -142.439697266 -10.061662674 0.810000002)
+      (-9.350039482 -145.010009766 -10.342617035 0.810000002)
+      (-9.276642799 -147.639053345 -10.435325623 0.810000002)
+      (-9.081604958 -149.520324707 -10.293729782 0.810000002)
+      (-8.807494164 -151.083572388 -10.059227943 0.810000002)
+      (-8.446282387 -152.678466797 -9.838175774 0.810000002)
+      (-7.713676453 -155.518814087 -9.502863884 0.740000010)
+      (-7.147308826 -157.705749512 -9.330964088 0.740000010)
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+      (-5.942045212 -163.408081055 -9.323469162 0.740000010)
+      (-5.620731354 -166.614135742 -9.802371025 0.660000026)
+      (-5.618084908 -167.628021240 -10.017097473 0.660000026)
+      (-5.805118561 -169.362396240 -10.492876053 0.660000026)
+      (-6.150900364 -170.452758789 -10.727066994 0.660000026)
+      (-6.982748985 -171.978118896 -10.831708908 0.660000026)
+      (-7.710608482 -173.287734985 -10.659621239 0.660000026)
+      (-9.417262077 -176.748474121 -9.718672752 0.660000026)
+      (-10.299280167 -178.632507324 -9.087595940 0.660000026)
+      (-11.264322281 -180.927444458 -8.165761948 0.660000026)
+      (-11.886566162 -182.662658691 -7.408018589 0.660000026)
+      (-13.011740685 -186.477172852 -5.616222382 0.660000026)
+      (-13.154414177 -187.158172607 -5.320770741 0.660000026)
+      (
+        (-13.154414177 -187.158172607 -5.320770741 0.660000026)
+        (-13.185297012 -188.982498169 -5.589263439 0.660000026)
+        (-13.284524918 -190.459320068 -5.726956844 0.660000026)
+        (-13.444418907 -192.882873535 -6.024431229 0.660000026)
+        (-13.567398071 -195.322525024 -6.390244484 0.660000026)
+        (-13.630141258 -197.472274780 -6.768671989 0.660000026)
+        (-13.705839157 -200.065902710 -7.225236893 0.660000026)
+        (-13.835023880 -202.501434326 -7.614274025 0.660000026)
+        (-13.990693092 -205.281295776 -8.089993477 0.660000026)
+        (-14.077683449 -206.983734131 -8.394641876 0.660000026)
+        (-14.110038757 -208.885345459 -8.808866501 0.660000026)
+        (
+          (-14.110038757 -208.885345459 -8.808866501 0.660000026)
+          (-15.691842079 -213.824035645 -7.863440514 0.660000026)
+          (-17.002302170 -218.825012207 -7.035860062 0.660000026)
+          (-17.540596008 -221.748764038 -6.699442863 0.660000026)
+          (-17.815729141 -224.429611206 -6.531734467 0.660000026)
+          (-17.875246048 -226.107437134 -6.490512848 0.660000026)
+          (-17.865568161 -228.279083252 -6.497528076 0.660000026)
+          (-17.606994629 -231.119873047 -6.560680389 0.660000026)
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+; Created by MorphIO v3.4.0
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc
new file mode 100644
index 00000000..7b684b32
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.asc
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+      (-14.008646011 -102.522964478 97.413482666 0.289999992)
+    )
+  )
+)
+
+; Created by MorphIO v3.4.0
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5
new file mode 100644
index 00000000..a8adfeb8
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/h5/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/merged-morphologies.h5 b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/merged-morphologies.h5
new file mode 100644
index 00000000..2342a845
Binary files /dev/null and b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/merged-morphologies.h5 differ
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/swc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.swc b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/swc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.swc
new file mode 100644
index 00000000..fd909a92
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/morphologies/swc/dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.swc
@@ -0,0 +1,15260 @@
+# Created by MorphIO v3.4.0
+# index     type         X            Y            Z       radius       parent
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+15156           4 21.912603378  6.959074020 14.486137390  0.330000013       15155
+15157           4 22.378059387  5.917944431 15.029431343  0.330000013       15156
+15158           4 22.793836594  5.104351044 15.627360344  0.330000013       15157
+15159           4 23.592233658  3.838808060 16.957008362  0.330000013       15158
+15160           4 24.157382965  3.205582142 18.015594482  0.330000013       15159
+15161           4 24.794717789  2.664866447 19.180908203  0.330000013       15160
+15162           4 25.634859085  2.066327095 20.753711700  0.330000013       15161
+15163           4 26.015054703  1.807620525 21.510766983  0.405000001       15162
+15164           4 26.463464737  1.488998413 22.481639862  0.405000001       15163
+15165           4 27.503042221  0.642273903 25.084875107  0.405000001       15164
+15166           4 27.966123581  0.201858044 26.482954025  0.219999999       15165
+15167           4 28.500091553 -0.466222763 28.483680725  0.219999999       15166
+15168           4 28.768545151 -0.924431801 29.754171371  0.219999999       15167
+15169           4 28.983539581 -1.505067348 31.320240021  0.219999999       15168
+15170           4 29.074712753 -2.215001106 33.198219299  0.219999999       15169
+15171           4 29.003936768 -2.943823338 35.056270599  0.219999999       15170
+15172           4 28.838649750 -3.586054325 36.587024689  0.219999999       15171
+15173           4 28.615312576 -4.296045303 38.034397125  0.219999999       15172
+15174           4 28.323551178 -5.496803284 40.167148590  0.219999999       15173
+15175           4 28.168546677 -6.363686562 41.501678467  0.219999999       15174
+15176           4 28.096509933 -7.047448158 42.415756226  0.219999999       15175
+15177           4 28.000476837 -8.118192673 43.741485596  0.219999999       15176
+15178           4 27.921184540 -8.956985474 44.734401703  0.219999999       15177
+15179           4 27.808898926 -10.405576706 46.330394745  0.219999999       15178
+15180           4 27.695474625 -11.708212852 47.636196136  0.219999999       15179
+15181           4 27.458709717 -13.361906052 49.141601562  0.219999999       15180
+15182           4 27.099658966 -14.799883842 50.348751068  0.219999999       15181
+15183           4 26.551975250 -16.187469482 51.393348694  0.219999999       15182
+15184           4 25.944114685 -17.285921097 52.158840179  0.219999999       15183
+15185           4 25.172992706 -18.400970459 52.878417969  0.219999999       15184
+15186           4 24.048114777 -19.857826233 53.804054260  0.219999999       15185
+15187           4 23.188631058 -20.848829269 54.475299835  0.219999999       15186
+15188           4 22.295507431 -21.795578003 55.118003845  0.219999999       15187
+15189           4 21.040863037 -22.953834534 55.971431732  0.185000002       15188
+15190           4 20.074989319 -23.678096771 56.587852478  0.185000002       15189
+15191           4 18.741685867 -24.557800293 57.420864105  0.185000002       15190
+15192           4 17.128494263 -25.495975494 58.338497162  0.185000002       15191
+15193           4 15.584409714 -26.409807205 59.142208099  0.185000002       15192
+15194           4 14.728104591 -26.945148468 59.547676086  0.185000002       15193
+15195           4 13.126855850 -28.072052002 60.236343384  0.185000002       15194
+15196           4 12.007952690 -28.911252975 60.611846924  0.185000002       15195
+15197           4 10.696562767 -29.933849335 60.964855194  0.185000002       15196
+15198           4  9.462860107 -31.000968933 61.325332642  0.185000002       15197
+15199           4  8.754053116 -31.649951935 61.566104889  0.185000002       15198
+15200           4  7.564563751 -32.786495209 62.002956390  0.185000002       15199
+15201           4  5.361604214 -34.961719513 62.764739990  0.185000002       15200
+15202           4  4.061303139 -36.207176208 63.184638977  0.185000002       15201
+15203           4  2.961497307 -37.257469177 63.530467987  0.259999990       15202
+15204           4  1.135951757 -38.965278625 64.131645203  0.259999990       15203
+15205           4 -0.383671522 -40.451595306 64.733169556  0.259999990       15204
+15206           4 -1.391723633 -41.565814972 65.256217957  0.259999990       15205
+15207           4 -2.215602160 -42.766246796 65.911163330  0.259999990       15206
+15208           4 -2.803650856 -43.791557312 66.449279785  0.259999990       15207
+15209           4 -3.261564970 -44.709590912 66.910522461  0.185000002       15208
+15210           4 -3.658033133 -45.692485809 67.409645081  0.185000002       15209
+15211           4 -4.001552582 -46.688850403 67.953979492  0.185000002       15210
+15212           4 -4.412050724 -47.948894501 68.749778748  0.185000002       15211
+15213           4 -4.933276176 -49.389209747 69.757301331  0.185000002       15212
+15214           4 -5.131802082 -49.995979309 70.243606567  0.185000002       15213
+15215           4 -5.332300186 -50.910953522 70.994804382  0.185000002       15214
+15216           4 -5.580716610 -51.904663086 71.738784790  0.185000002       15215
+15217           4 -5.723751545 -52.556957245 72.270507812  0.185000002       15216
+15218           4 -5.927055359 -53.642639160 73.235618591  0.185000002       15217
+15219           4 -6.360799313 -55.309169769 74.712051392  0.185000002       15218
+15220           4 -6.969802856 -57.043926239 76.059677124  0.185000002       15219
+15221           4 -8.045385361 -59.469802856 77.594581604  0.185000002       15220
+15222           4 -8.807374001 -61.001884460 78.459785461  0.185000002       15221
+15223           4 -9.290958405 -61.964241028 79.002357483  0.185000002       15222
+15224           4 -10.104702950 -63.560241699 80.001327515  0.185000002       15223
+15225           4 -10.906383514 -64.994010925 81.115158081  0.185000002       15224
+15226           4 -11.296949387 -65.823768616 82.018707275  0.185000002       15225
+15227           4 -11.470261574 -66.696594238 83.174934387  0.185000002       15226
+15228           4 -11.511389732 -67.586791992 84.383796692  0.185000002       15227
+15229           4 -11.328992844 -68.372726440 85.748138428  0.185000002       15228
+15230           4 -11.059059143 -68.932197571 87.004234314  0.185000002       15229
+15231           4 -10.873677254 -69.268432617 87.942749023  0.185000002       15230
+15232           4 -10.662151337 -69.771186829 89.375488281  0.185000002       15231
+15233           4 -10.594342232 -70.122879028 90.225204468  0.185000002       15232
+15234           4 -10.551586151 -71.230880737 92.426345825  0.185000002       15233
+15235           4 -10.570245743 -71.883468628 93.608139038  0.185000002       15234
+15236           4 -10.627932549 -72.578208923 94.900558472  0.185000002       15235
+15237           4 -10.751924515 -73.335830688 96.321556091  0.185000002       15236
+15238           4 -10.906521797 -73.971725464 97.468284607  0.185000002       15237
+15239           4 -11.133865356 -74.899353027 98.938385010  0.185000002       15238
+15240           4 -11.263077736 -75.520553589 99.764350891  0.185000002       15239
+15241           4 -11.402597427 -76.861236572 101.303520203  0.185000002       15240
+15242           4 -11.389368057 -77.769706726 102.171936035  0.185000002       15241
+15243           4 -11.350323677 -78.299797058 102.482025146  0.185000002       15242
+15244           4 -11.324575424 -79.317924500 102.418685913  0.185000002       15243
+15245           4 -11.333949089 -79.990493774 102.185310364  0.185000002       15244
+15246           4 -11.262062073 -81.318847656 101.725807190  0.185000002       15245
+15247           4 -11.068578720 -82.761497498 101.252441406  0.185000002       15246
+15248           4 -10.806444168 -84.748229980 100.600097656  0.185000002       15247
+15249           4 -10.743649483 -86.208175659 100.130264282  0.185000002       15248
+15250           4 -10.777109146 -87.603935242 99.732307434  0.185000002       15249
+15251           4 -10.925455093 -89.938079834 99.214073181  0.185000002       15250
+15252           4 -11.130138397 -91.944496155 98.897628784  0.185000002       15251
+15253           4 -11.432189941 -93.988723755 98.661766052  0.185000002       15252
+15254           4 -11.971424103 -97.092063904 98.330795288  0.185000002       15253
+15255           4 -12.272184372 -98.383491516 98.187469482  0.185000002       15254
+15256           4 -12.687401772 -99.683822632 97.975799561  0.144999996       15255
+15257           4 -13.339817047 -101.212097168 97.651489258  0.144999996       15256
+15258           4 -14.008646011 -102.522964478 97.413482666  0.144999996       15257
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/node_sets.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/node_sets.json
new file mode 100644
index 00000000..ef08b4d4
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/node_sets.json
@@ -0,0 +1,737 @@
+{
+  "Mosaic": [
+    "All"
+  ],
+  "All": [
+    "L4_SBC",
+    "L5_TPC:B",
+    "L4_NBC",
+    "L4_DBC",
+    "L23_DBC",
+    "L5_TPC:A",
+    "L3_TPC:A",
+    "L4_BTC",
+    "L6_BTC",
+    "L4_UPC",
+    "L6_LBC",
+    "L6_IPC",
+    "L23_BP",
+    "L4_MC",
+    "L5_DBC",
+    "L6_NBC",
+    "L1_LAC",
+    "L6_CHC",
+    "L23_MC",
+    "L1_HAC",
+    "L6_NGC",
+    "L4_NGC",
+    "L2_IPC",
+    "L6_HPC",
+    "L5_BTC",
+    "L4_CHC",
+    "L5_NGC",
+    "L4_BP",
+    "L2_TPC:A",
+    "L5_SBC",
+    "L5_UPC",
+    "L6_DBC",
+    "L23_CHC",
+    "L5_NBC",
+    "L1_NGC-SA",
+    "L6_BPC",
+    "L6_UPC",
+    "L23_LBC",
+    "L4_SSC",
+    "L5_LBC",
+    "L6_SBC",
+    "L1_NGC-DA",
+    "L6_TPC:C",
+    "L23_SBC",
+    "L5_MC",
+    "L1_DAC",
+    "L2_TPC:B",
+    "L6_TPC:A",
+    "L5_CHC",
+    "L23_NBC",
+    "L3_TPC:C",
+    "L6_BP",
+    "L4_LBC",
+    "L5_BP",
+    "L23_BTC",
+    "L6_MC",
+    "L1_SAC",
+    "L4_TPC",
+    "L5_TPC:C",
+    "L23_NGC"
+  ],
+  "Excitatory": {
+    "synapse_class": "EXC"
+  },
+  "Inhibitory": {
+    "synapse_class": "INH"
+  },
+  "L1_DAC": {
+    "mtype": "L1_DAC"
+  },
+  "L1_HAC": {
+    "mtype": "L1_HAC"
+  },
+  "L1_LAC": {
+    "mtype": "L1_LAC"
+  },
+  "L1_NGC-DA": {
+    "mtype": "L1_NGC-DA"
+  },
+  "L1_NGC-SA": {
+    "mtype": "L1_NGC-SA"
+  },
+  "L1_SAC": {
+    "mtype": "L1_SAC"
+  },
+  "L23_BP": {
+    "mtype": "L23_BP"
+  },
+  "L23_BTC": {
+    "mtype": "L23_BTC"
+  },
+  "L23_CHC": {
+    "mtype": "L23_CHC"
+  },
+  "L23_DBC": {
+    "mtype": "L23_DBC"
+  },
+  "L23_LBC": {
+    "mtype": "L23_LBC"
+  },
+  "L23_MC": {
+    "mtype": "L23_MC"
+  },
+  "L23_NBC": {
+    "mtype": "L23_NBC"
+  },
+  "L23_NGC": {
+    "mtype": "L23_NGC"
+  },
+  "L23_SBC": {
+    "mtype": "L23_SBC"
+  },
+  "L2_IPC": {
+    "mtype": "L2_IPC"
+  },
+  "L2_TPC:A": {
+    "mtype": "L2_TPC:A"
+  },
+  "L2_TPC:B": {
+    "mtype": "L2_TPC:B"
+  },
+  "L3_TPC:A": {
+    "mtype": "L3_TPC:A"
+  },
+  "L3_TPC:C": {
+    "mtype": "L3_TPC:C"
+  },
+  "L4_BP": {
+    "mtype": "L4_BP"
+  },
+  "L4_BTC": {
+    "mtype": "L4_BTC"
+  },
+  "L4_CHC": {
+    "mtype": "L4_CHC"
+  },
+  "L4_DBC": {
+    "mtype": "L4_DBC"
+  },
+  "L4_LBC": {
+    "mtype": "L4_LBC"
+  },
+  "L4_MC": {
+    "mtype": "L4_MC"
+  },
+  "L4_NBC": {
+    "mtype": "L4_NBC"
+  },
+  "L4_NGC": {
+    "mtype": "L4_NGC"
+  },
+  "L4_SBC": {
+    "mtype": "L4_SBC"
+  },
+  "L4_SSC": {
+    "mtype": "L4_SSC"
+  },
+  "L4_TPC": {
+    "mtype": "L4_TPC"
+  },
+  "L4_UPC": {
+    "mtype": "L4_UPC"
+  },
+  "L5_BP": {
+    "mtype": "L5_BP"
+  },
+  "L5_BTC": {
+    "mtype": "L5_BTC"
+  },
+  "L5_CHC": {
+    "mtype": "L5_CHC"
+  },
+  "L5_DBC": {
+    "mtype": "L5_DBC"
+  },
+  "L5_LBC": {
+    "mtype": "L5_LBC"
+  },
+  "L5_MC": {
+    "mtype": "L5_MC"
+  },
+  "L5_NBC": {
+    "mtype": "L5_NBC"
+  },
+  "L5_NGC": {
+    "mtype": "L5_NGC"
+  },
+  "L5_SBC": {
+    "mtype": "L5_SBC"
+  },
+  "L5_TPC:A": {
+    "mtype": "L5_TPC:A"
+  },
+  "L5_TPC:B": {
+    "mtype": "L5_TPC:B"
+  },
+  "L5_TPC:C": {
+    "mtype": "L5_TPC:C"
+  },
+  "L5_UPC": {
+    "mtype": "L5_UPC"
+  },
+  "L6_BP": {
+    "mtype": "L6_BP"
+  },
+  "L6_BPC": {
+    "mtype": "L6_BPC"
+  },
+  "L6_BTC": {
+    "mtype": "L6_BTC"
+  },
+  "L6_CHC": {
+    "mtype": "L6_CHC"
+  },
+  "L6_DBC": {
+    "mtype": "L6_DBC"
+  },
+  "L6_HPC": {
+    "mtype": "L6_HPC"
+  },
+  "L6_IPC": {
+    "mtype": "L6_IPC"
+  },
+  "L6_LBC": {
+    "mtype": "L6_LBC"
+  },
+  "L6_MC": {
+    "mtype": "L6_MC"
+  },
+  "L6_NBC": {
+    "mtype": "L6_NBC"
+  },
+  "L6_NGC": {
+    "mtype": "L6_NGC"
+  },
+  "L6_SBC": {
+    "mtype": "L6_SBC"
+  },
+  "L6_TPC:A": {
+    "mtype": "L6_TPC:A"
+  },
+  "L6_TPC:C": {
+    "mtype": "L6_TPC:C"
+  },
+  "L6_UPC": {
+    "mtype": "L6_UPC"
+  },
+  "bAC": {
+    "etype": "bAC"
+  },
+  "bIR": {
+    "etype": "bIR"
+  },
+  "bNAC": {
+    "etype": "bNAC"
+  },
+  "bSTUT": {
+    "etype": "bSTUT"
+  },
+  "cACint": {
+    "etype": "cACint"
+  },
+  "cADpyr": {
+    "etype": "cADpyr"
+  },
+  "cIR": {
+    "etype": "cIR"
+  },
+  "cNAC": {
+    "etype": "cNAC"
+  },
+  "cSTUT": {
+    "etype": "cSTUT"
+  },
+  "dNAC": {
+    "etype": "dNAC"
+  },
+  "dSTUT": {
+    "etype": "dSTUT"
+  },
+  "Layer1": {
+    "layer": "1"
+  },
+  "Layer2": {
+    "layer": "2"
+  },
+  "Layer3": {
+    "layer": "3"
+  },
+  "Layer4": {
+    "layer": "4"
+  },
+  "Layer5": {
+    "layer": "5"
+  },
+  "Layer6": {
+    "layer": "6"
+  },
+  "Layer2Excitatory": [
+    "L2_TPC:A",
+    "L2_TPC:B",
+    "L2_IPC"
+  ],
+  "Layer3Excitatory": [
+    "L3_TPC:C",
+    "L3_TPC:A"
+  ],
+  "Layer23Excitatory": [
+    "Layer3Excitatory",
+    "Layer2Excitatory"
+  ],
+  "Layer4Excitatory": [
+    "L4_UPC",
+    "L4_TPC",
+    "L4_SSC"
+  ],
+  "Layer5Excitatory": [
+    "L5_UPC",
+    "L5_TPC:A",
+    "L5_TPC:B",
+    "L5_TPC:C"
+  ],
+  "Layer6Excitatory": [
+    "L6_BPC",
+    "L6_HPC",
+    "L6_UPC",
+    "L6_TPC:C",
+    "L6_IPC",
+    "L6_TPC:A"
+  ],
+  "Layer1Inhibitory": [
+    "L1_HAC",
+    "L1_NGC-SA",
+    "L1_NGC-DA",
+    "L1_DAC",
+    "L1_SAC",
+    "L1_LAC"
+  ],
+  "Layer23Inhibitory": [
+    "L23_NGC",
+    "L23_DBC",
+    "L23_LBC",
+    "L23_BTC",
+    "L23_SBC",
+    "L23_BP",
+    "L23_MC",
+    "L23_NBC",
+    "L23_CHC"
+  ],
+  "Layer4Inhibitory": [
+    "L4_SBC",
+    "L4_NBC",
+    "L4_DBC",
+    "L4_NGC",
+    "L4_LBC",
+    "L4_BTC",
+    "L4_CHC",
+    "L4_BP",
+    "L4_MC"
+  ],
+  "Layer5Inhibitory": [
+    "L5_NBC",
+    "L5_BTC",
+    "L5_BP",
+    "L5_LBC",
+    "L5_NGC",
+    "L5_MC",
+    "L5_DBC",
+    "L5_SBC",
+    "L5_CHC"
+  ],
+  "Layer6Inhibitory": [
+    "L6_NGC",
+    "L6_BP",
+    "L6_BTC",
+    "L6_SBC",
+    "L6_LBC",
+    "L6_MC",
+    "L6_NBC",
+    "L6_DBC",
+    "L6_CHC"
+  ],
+  "hex0": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ]
+  },
+  "hex1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex2": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex3": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex_O1": [
+    "hex6",
+    "hex0",
+    "hex4",
+    "hex2",
+    "hex3",
+    "hex5",
+    "hex1"
+  ],
+  "hex_O1PV": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex_O1Sst": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "hex_O15HT3aR": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "proj_Thalamocortical_VPM_Source": {
+    "population": "VPM"
+  },
+  "proj_Thalamocortical_POM_Source": {
+    "population": "POm"
+  },
+  "nbS1-HEX0": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ]
+  },
+  "nbS1-HEX1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX2": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX3": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": []
+  },
+  "nbS1-HEX0-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX1-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX2-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX3-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX4-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX5-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX6-L1": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "1"
+    ]
+  },
+  "nbS1-HEX0-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX1-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX2-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX3-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX4-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX5-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX6-L23": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "2",
+      "3"
+    ]
+  },
+  "nbS1-HEX0-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX1-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX2-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX3-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX4-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX5-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX6-L4": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "4"
+    ]
+  },
+  "nbS1-HEX0-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX1-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX2-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX3-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX4-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX5-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX6-L5": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "5"
+    ]
+  },
+  "nbS1-HEX0-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [
+      0
+    ],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX1-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX2-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX3-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX4-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX5-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  },
+  "nbS1-HEX6-L6": {
+    "population": "S1nonbarrel_neurons",
+    "node_id": [],
+    "layer": [
+      "6"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/run_coordinate_instance.json b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/run_coordinate_instance.json
new file mode 100644
index 00000000..33042104
--- /dev/null
+++ b/tests/examples/container_nbS1-O1__202247__cADpyr__L5_TPC_A/run_coordinate_instance.json
@@ -0,0 +1,51 @@
+{
+    "obi_one_version": "2025.10.8",
+    "type": "CircuitExtractionSingleConfig",
+    "idx": 0,
+    "coordinate_output_root": "obi-output/extracted_synaptome_nbS1-O1/nbS1-O1__202247__cADpyr__L5_TPC:A",
+    "scan_output_root": "obi-output/extracted_synaptome_nbS1-O1",
+    "single_coordinate_scan_params": {
+        "scan_params": [
+            {
+                "location_list": [
+                    "initialize",
+                    "circuit"
+                ],
+                "type": "SingleValueScanParam",
+                "value": {
+                    "name": "nbS1-O1__202247__cADpyr__L5_TPC:A",
+                    "path": "/Volumes/T7/nbS1/nbS1-O1/circuit_config_postfix2.json",
+                    "matrix_path": null,
+                    "type": "Circuit"
+                }
+            }
+        ],
+        "nested_coordinate_subpath_str": "nbS1-O1__202247__cADpyr__L5_TPC:A/",
+        "type": "SingleCoordinateScanParams"
+    },
+    "initialize": {
+        "type": "CircuitExtractionScanConfig.Initialize",
+        "circuit": {
+            "name": "nbS1-O1__202247__cADpyr__L5_TPC:A",
+            "path": "/Volumes/T7/nbS1/nbS1-O1/circuit_config_postfix2.json",
+            "matrix_path": null,
+            "type": "Circuit"
+        },
+        "run_validation": false,
+        "do_virtual": true,
+        "create_external": true,
+        "virtual_sources_to_ignore": []
+    },
+    "neuron_set": {
+        "type": "IDNeuronSet",
+        "sample_percentage": 100.0,
+        "sample_seed": 1,
+        "neuron_ids": {
+            "name": "nbS1-O1__202247__cADpyr__L5_TPC:A",
+            "elements": [
+                202247
+            ],
+            "type": "NamedTuple"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/test_cell/test_morphology_wrapper.py b/tests/test_cell/test_morphology_wrapper.py
new file mode 100644
index 00000000..03e7c17c
--- /dev/null
+++ b/tests/test_cell/test_morphology_wrapper.py
@@ -0,0 +1,463 @@
+"""Tests for H5 morphology wrapper functionality."""
+
+import pytest
+from pathlib import Path
+from unittest.mock import patch
+
+from bluecellulab.cell.morphio_wrapper import (
+    MorphIOWrapper,
+    SectionName,
+    split_morphology_path,
+)
+
+
+class TestMorphologyWrapper:
+    """Test suite for MorphIOWrapper class."""
+
+    def test_split_morphology_path_file_exists(self, tmp_path):
+        """Test split_morphology_path with existing file."""
+        morph_file = tmp_path / "test_morph.h5"
+        morph_file.write_bytes(b"dummy content")
+
+        collection_dir, morph_name, morph_ext = split_morphology_path(morph_file)
+
+        assert collection_dir == str(tmp_path)
+        assert morph_name == "test_morph"
+        assert morph_ext == ".h5"
+
+    def test_split_morphology_path_directory(self, tmp_path):
+        """Test split_morphology_path with directory path."""
+        # Create the directory and file for testing
+        container_dir = tmp_path / "container_dir"
+        container_dir.mkdir()
+        morph_path = container_dir / "test_morph.h5"
+        morph_path.write_bytes(b"dummy content")
+
+        collection_dir_result, morph_name, morph_ext = split_morphology_path(morph_path)
+
+        assert collection_dir_result == str(container_dir)
+        assert morph_name == "test_morph"
+        assert morph_ext == ".h5"
+
+    def test_split_morphology_path_error_invalid_path(self):
+        """Test split_morphology_path raises error for invalid path."""
+        from bluecellulab.exceptions import BluecellulabError
+
+        # Empty string triggers the error because dirname("") == ""
+        with pytest.raises(BluecellulabError, match="Failed to split path"):
+            split_morphology_path("")
+
+    def test_split_morphology_path_container_with_cell_name(self, tmp_path):
+        """Test split_morphology_path with H5 container and cell name."""
+        container_file = tmp_path / "container.h5"
+        container_file.write_bytes(b"dummy h5 content")
+
+        cell_path = f"{container_file}/cell_name"
+        collection_dir, morph_name, morph_ext = split_morphology_path(cell_path)
+
+        assert collection_dir == str(container_file)
+        assert morph_name == "cell_name"
+        assert morph_ext == ""
+
+    def test_morphology_wrapper_init_success(self):
+        """Test successful MorphologyWrapper initialization with real H5 file."""
+        # Test with a real H5 file from BlueCelluLab test data
+        h5_file = Path(__file__).parent.parent / "examples" / "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" / "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        assert h5_file.exists(), f"Test file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(h5_file)
+
+        # Test basic properties
+        assert wrapper._morph_ext == ".h5"
+        assert wrapper._morph_name == "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0"
+        assert len(wrapper._section_names) > 0
+        assert len(wrapper.morph_as_hoc()) > 0
+
+        # Test section names are SectionName objects
+        for section_name in wrapper._section_names:
+            assert isinstance(section_name, SectionName)
+            assert hasattr(section_name, 'name')
+            assert hasattr(section_name, 'id')
+
+        # Test HOC commands are valid
+        commands = wrapper.morph_as_hoc()
+        assert any('create' in cmd for cmd in commands)
+        assert any('pt3dadd' in cmd for cmd in commands)
+
+    def test_morphology_wrapper_individual_h5_files(self):
+        """Test MorphologyWrapper with individual H5 file."""
+        h5_file = (
+            Path(__file__).parent.parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" /
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        )
+        assert h5_file.exists(), f"Test file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(h5_file)
+
+        # Test that file loads successfully
+        assert wrapper._morph_ext == ".h5"
+        assert len(wrapper._section_names) > 0
+        assert len(wrapper.morph_as_hoc()) > 0
+
+        # Test that morph property is accessible
+        assert wrapper.morph is not None
+
+    def test_morphology_wrapper_h5_container(self):
+        """Test MorphologyWrapper with H5 container."""
+        container_file = (
+            Path(__file__).parent.parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "merged-morphologies.h5"
+        )
+
+        try:
+            # Test that container file exists and can be accessed
+            import h5py
+            with h5py.File(container_file, 'r') as f:
+                assert len(f.keys()) > 0, "Container should have cells"
+                # Test that we can access cell names (but don't load them due to length issues)
+                cell_names = list(f.keys())
+                assert len(cell_names) > 0
+
+            # Test that MorphIOWrapper can handle container path format
+            # Use a simple test path to verify path parsing works
+            test_path = f"{container_file}/test_cell"
+            collection_dir, morph_name, morph_ext = split_morphology_path(test_path)
+
+            # Verify path parsing works for containers
+            assert collection_dir == str(container_file)
+            assert morph_name == "test_cell"
+            assert morph_ext == ""
+
+        except FileNotFoundError:
+            pytest.skip("Container test file not available")
+        except Exception as e:
+            if "MorphIO is not available" in str(e):
+                pytest.skip("MorphIO not available")
+            else:
+                raise
+
+    def test_morphology_wrapper_no_morphio(self, tmp_path):
+        """Test MorphologyWrapper when MorphIO is not available."""
+        # Create a temporary H5 file so path splitting succeeds
+        h5_file = tmp_path / "test.h5"
+        h5_file.write_bytes(b"dummy")
+
+        # Mock the morphio import to raise ImportError
+        import sys
+        original_modules = sys.modules.copy()
+
+        # Ensure branch below is always executed
+        sys.modules['morphio'] = object()
+
+        # Remove morphio from sys.modules if it exists
+        if 'morphio' in sys.modules:
+            del sys.modules['morphio']
+
+        # Mock the import to fail
+        with patch.dict('sys.modules', {'morphio': None}):
+            with pytest.raises(RuntimeError, match="MorphIO is not available"):
+                MorphIOWrapper(str(h5_file))
+
+        # Restore original modules
+        sys.modules.update(original_modules)
+
+    def test_morphology_wrapper_unsupported_extension(self):
+        """Test MorphIOWrapper with unsupported file extension."""
+        # Neurodamus doesn't validate extension, it will fail when trying to load
+        # Just test that it doesn't crash on init
+        try:
+            wrapper = MorphIOWrapper("test.h5")
+            # If it gets here, it will fail when trying to load the morphology
+        except Exception:
+            # Expected to fail when loading non-H5 file
+            pass
+
+    def test_morph_as_hoc(self):
+        """Test HOC command generation with morph_as_hoc method."""
+        h5_file = (
+            Path(__file__).parent.parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" /
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        )
+        assert h5_file.exists(), f"Test file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(h5_file)
+
+        # Test that morph_as_hoc generates HOC commands
+        commands = wrapper.morph_as_hoc()
+
+        assert isinstance(commands, list)
+        assert len(commands) > 0
+
+        # Verify sections were created
+        import neuron
+        for cmd in commands:
+            neuron.h(cmd)
+
+        # Check that soma exists
+        soma_exists = False
+        for sec in neuron.h.allsec():
+            if 'soma' in neuron.h.secname(sec=sec):
+                soma_exists = True
+                break
+
+        assert soma_exists, "Soma section should exist in NEURON after loading"
+
+    def test_morph_property(self):
+        """Test morph property access."""
+        h5_file = (
+            Path(__file__).parent.parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" /
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        )
+        assert h5_file.exists(), f"Test file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(h5_file)
+
+        # Test morph property
+        assert wrapper.morph is not None
+        assert hasattr(wrapper.morph, 'soma')
+        assert hasattr(wrapper.morph, 'sections')
+
+    def test_section_name_objects(self):
+        """Test that section names are SectionName objects."""
+        h5_file = (
+            Path(__file__).parent.parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" /
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        )
+        assert h5_file.exists(), f"Test file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(h5_file)
+
+        # Test that _section_names contains SectionName objects
+        assert len(wrapper._section_names) > 0
+        assert isinstance(wrapper._section_names[0], SectionName)
+        assert wrapper._section_names[0].name == "soma"
+        assert wrapper._section_names[0].id == 0
+
+        # Test string representation
+        assert str(wrapper._section_names[0]) == "soma[0]"
+
+
+class TestCellH5Integration:
+    """Test integration of H5 morphologies with Cell class."""
+
+    def test_cell_h5_detection_simple(self):
+        """Test simple H5 detection without Cell integration."""
+        # Test the simple string check that replaced is_h5_morphology
+        assert '.h5' in "test.h5".lower()
+        assert '.h5' in "test.H5".lower()
+        assert '.h5' in "container.h5/cell_name".lower()
+        assert '.h5' not in "test.asc".lower()
+        assert '.h5' not in "test.swc".lower()
+        assert '.h5' not in "test.txt".lower()
+
+    def test_cell_h5_automatic_detection(self):
+        """Test that Cell automatically detects and loads H5 morphologies."""
+        # This is a simple test to verify the H5 detection logic in Cell.__init__
+        # The actual Cell integration is tested elsewhere
+
+        # Just verify the detection logic works
+        test_paths = [
+            "test.h5",
+            "container.h5/cell_name",
+            "path/to/morph.H5",
+        ]
+
+        for path in test_paths:
+            assert '.h5' in str(path).lower(), f"Should detect H5 in {path}"
+
+    def test_type2name_method(self):
+        """Test type2name class method."""
+        # Test known types
+        assert MorphIOWrapper.type2name(1) == "soma"
+        assert MorphIOWrapper.type2name(2) == "axon"
+        assert MorphIOWrapper.type2name(3) == "dend"
+        assert MorphIOWrapper.type2name(4) == "apic"
+
+        # Test unknown types
+        assert MorphIOWrapper.type2name(5) == "dend_5"
+        assert MorphIOWrapper.type2name(10) == "dend_10"
+
+        # Test negative types
+        assert MorphIOWrapper.type2name(-1) == "minus_1"
+        assert MorphIOWrapper.type2name(-5) == "minus_5"
+
+    def test_mksubset_method(self):
+        """Test mksubset class method."""
+        # Test known types
+        assert MorphIOWrapper.mksubset(1, "soma") == 'forsec "soma" somatic.append'
+        assert MorphIOWrapper.mksubset(2, "axon") == 'forsec "axon" axonal.append'
+        assert MorphIOWrapper.mksubset(3, "dend") == 'forsec "dend" basal.append'
+        assert MorphIOWrapper.mksubset(4, "apic") == 'forsec "apic" apical.append'
+
+        # Test unknown types
+        assert MorphIOWrapper.mksubset(5, "unknown") == 'forsec "unknown" dendritic_5.append'
+        assert MorphIOWrapper.mksubset(10, "custom") == 'forsec "custom" dendritic_10.append'
+
+        # Test negative types
+        assert MorphIOWrapper.mksubset(-1, "negative") == 'forsec "negative" minus_1set.append'
+
+    def test_section_name_dataclass(self):
+        """Test SectionName dataclass functionality."""
+        # Test SectionName creation and attributes
+        section_name = SectionName(name="soma", id=0)
+        assert section_name.name == "soma"
+        assert section_name.id == 0
+
+        # Test string representation
+        assert str(section_name) == "soma[0]"
+
+        # Test equality
+        section_name2 = SectionName(name="soma", id=0)
+        assert section_name == section_name2
+
+        # Test inequality
+        section_name3 = SectionName(name="dend", id=1)
+        assert section_name != section_name3
+
+    def test_morphio_wrapper_with_single_point_soma(self):
+        """Test MorphIOWrapper with single point soma morphology."""
+        # This tests the soma conversion functions (_to_sphere, single_point_sphere_to_circular_contour)
+        # which are called for SOMA_SINGLE_POINT type morphologies
+        h5_file = Path(__file__).parent.parent / "examples" / "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" / "h5" / "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+
+        if h5_file.exists():
+            # Initialize wrapper - this will trigger soma conversion if needed
+            wrapper = MorphIOWrapper(h5_file)
+
+            # Verify the wrapper was created successfully
+            assert wrapper is not None
+            assert hasattr(wrapper, '_morph')
+
+            # The soma should have been converted to a circular contour
+            # Verify basic morphology properties
+            hoc_commands = wrapper.morph_as_hoc()
+            assert len(hoc_commands) > 0
+            assert any('soma' in cmd for cmd in hoc_commands)
+
+    def test_make_convex_edge_cases(self):
+        """Test make_convex function with edge cases."""
+        from bluecellulab.cell.morphio_wrapper import make_convex
+        import numpy as np
+
+        # Test case where convex_idx returns False for some indices (line 97)
+        # Create non-convex data where some points need to be filtered
+        sides = [
+            np.array([1.0, 3.0, 2.0, 4.0]),  # Non-monotonic - will trigger line 97
+            np.array([1.0, 2.0, 3.0, 4.0])   # Monotonic
+        ]
+        rads = [
+            np.array([0.5, 0.6, 0.7, 0.8]),
+            np.array([0.5, 0.6, 0.7, 0.8])
+        ]
+
+        result_sides, result_rads = make_convex(sides, rads)
+
+        # Verify the function returns filtered arrays
+        assert len(result_sides) == 2
+        assert len(result_rads) == 2
+        # First side should be filtered to remove non-convex point
+        assert len(result_sides[0]) <= len(sides[0])
+
+    def test_to_sphere_and_single_point_conversion(self):
+        """Test _to_sphere and single_point_sphere_to_circular_contour functions."""
+        import numpy as np
+        try:
+            import morphio  # noqa: F401
+        except ImportError:
+            pytest.skip("morphio not available")
+
+        # Test the _to_sphere function directly
+        from bluecellulab.cell.morphio_wrapper import _to_sphere, single_point_sphere_to_circular_contour
+
+        # Create a mock neuron object with single point soma
+        class MockSoma:
+            def __init__(self):
+                self.points = np.array([[0.0, 0.0, 0.0]])
+                self.diameters = np.array([10.0])  # Diameter of 10, radius 5
+
+        class MockNeuron:
+            def __init__(self):
+                self.soma = MockSoma()
+
+        neuron = MockNeuron()
+
+        # Call _to_sphere - this should convert single point to circular contour
+        _to_sphere(neuron)
+
+        # Verify the soma has been converted to 20 points in a circle
+        assert len(neuron.soma.points) == 20
+        assert len(neuron.soma.diameters) == 20
+
+        # Verify points form a circle with radius 5.0
+        radius = neuron.soma.diameters[0]
+        assert radius == 5.0
+
+        # Test single_point_sphere_to_circular_contour wrapper function
+        neuron2 = MockNeuron()
+        single_point_sphere_to_circular_contour(neuron2)
+
+        # Should also have 20 points after conversion
+        assert len(neuron2.soma.points) == 20
+
+    def test_contourcenter_function(self):
+        """Test contourcenter utility function."""
+        import numpy as np
+        from bluecellulab.cell.morphio_wrapper import contourcenter
+
+        xyz = np.array([
+            [0.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0],
+            [1.0, 1.0, 0.0],
+            [0.0, 1.0, 0.0]
+        ])
+
+        mean, new_xyz = contourcenter(xyz)
+
+        assert mean.shape == (3,)
+        assert new_xyz.shape == (101, 3)
+
+    def test_get_sides_function(self):
+        """Test get_sides utility function."""
+        import numpy as np
+        from bluecellulab.cell.morphio_wrapper import get_sides
+
+        points = np.array([
+            [0.0, 0.0, 0.0],
+            [1.0, 1.0, 0.0],
+            [2.0, 0.0, 0.0],
+            [1.0, -1.0, 0.0]
+        ])
+
+        major = np.array([1.0, 0.0, 0.0])
+        minor = np.array([0.0, 1.0, 0.0])
+
+        sides, rads = get_sides(points, major, minor)
+
+        assert len(sides) == 2
+        assert len(rads) == 2
+        assert len(sides[0]) > 0
+        assert len(sides[1]) > 0
+
+    def test_contour2centroid_function(self):
+        """Test contour2centroid utility function."""
+        import numpy as np
+        from bluecellulab.cell.morphio_wrapper import contour2centroid
+
+        N = 20
+        radius = 5.0
+        phase = 2 * np.pi / (N - 1) * np.arange(N)
+        points = np.zeros((N, 3))
+        points[:, 0] = radius * np.cos(phase)
+        points[:, 1] = radius * np.sin(phase)
+
+        mean = np.array([0.0, 0.0, 0.0])
+
+        result_points, diameters = contour2centroid(mean, points)
+
+        assert result_points.shape[0] == 21
+        assert diameters.shape[0] == 21
+        assert np.all(diameters > 0)
diff --git a/tests/test_circuit/test_circuit_access.py b/tests/test_circuit/test_circuit_access.py
index 081db72a..ea2070ae 100644
--- a/tests/test_circuit/test_circuit_access.py
+++ b/tests/test_circuit/test_circuit_access.py
@@ -14,10 +14,12 @@
 """Unit tests for the circuit_access module."""
 
 from pathlib import Path
+from types import SimpleNamespace
 from unittest.mock import patch
 
 import pandas as pd
 import pytest
+from bluepysnap.exceptions import BluepySnapError
 
 from bluecellulab.circuit import CellId, SonataCircuitAccess
 from bluecellulab.circuit.circuit_access import EmodelProperties, get_synapse_connection_parameters
@@ -236,3 +238,141 @@ def test_get_emodel_properties_soma_scaler():
     circuit_access = SonataCircuitAccess(circuit_sonata_quick_scx_config)
     assert circuit_access.get_emodel_properties(CellId("NodeA", 0)).soma_scaler == 1.0
     assert circuit_access.get_emodel_properties(CellId("NodeA", 1)).soma_scaler == 1.002
+
+
+def test_get_target_cell_ids_with_population_filter():
+    access = object.__new__(SonataCircuitAccess)
+    access.config = SimpleNamespace(
+        get_node_sets=lambda: {"target": {"population": "popA", "node_id": [1, 2]}}
+    )
+    access._circuit = SimpleNamespace(
+        nodes={"popA": SimpleNamespace(ids=lambda _: [1, 2])}
+    )
+
+    result = SonataCircuitAccess.get_target_cell_ids(access, "target")
+
+    assert result == {CellId("popA", 1), CellId("popA", 2)}
+
+
+def test_get_target_cell_ids_without_population_filter():
+    access = object.__new__(SonataCircuitAccess)
+    access.config = SimpleNamespace(get_node_sets=lambda: {"target": {"layer": "L23"}})
+    access._circuit = SimpleNamespace(
+        nodes=SimpleNamespace(
+            ids=lambda _: [
+                SimpleNamespace(population="popA", id=1),
+                SimpleNamespace(population="popB", id=5),
+            ]
+        )
+    )
+
+    result = SonataCircuitAccess.get_target_cell_ids(access, "target")
+
+    assert result == {CellId("popA", 1), CellId("popB", 5)}
+
+
+def test_morph_filepath_h5v1_container_path():
+    access = object.__new__(SonataCircuitAccess)
+    node_population = SimpleNamespace(
+        config={"alternate_morphologies": {"h5v1": "/data/merged-morphologies.h5"}},
+        get=lambda _: {"morphology": "cell_42"},
+        morph=SimpleNamespace(get_filepath=lambda *_args, **_kwargs: "unused"),
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    result = SonataCircuitAccess.morph_filepath(access, CellId("popA", 42))
+
+    assert result == "/data/merged-morphologies.h5/cell_42.h5"
+
+
+def test_morph_filepath_h5v1_empty_name_falls_back_to_asc():
+    access = object.__new__(SonataCircuitAccess)
+    node_population = SimpleNamespace(
+        config={
+            "alternate_morphologies": {
+                "h5v1": "/data/merged-morphologies.h5",
+                "neurolucida-asc": "/data/asc",
+            }
+        },
+        get=lambda _: {"morphology": ""},
+        morph=SimpleNamespace(get_filepath=lambda *_args, **kwargs: "/data/asc/cell.asc" if kwargs.get("extension") == "asc" else "/data/swc/cell.swc"),
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    result = SonataCircuitAccess.morph_filepath(access, CellId("popA", 42))
+
+    assert result == "/data/asc/cell.asc"
+
+
+def test_morph_filepath_asc_error_falls_back_to_default():
+    access = object.__new__(SonataCircuitAccess)
+
+    def _get_filepath(*_args, **kwargs):
+        if kwargs.get("extension") == "asc":
+            raise BluepySnapError("asc not available")
+        return "/data/swc/cell.swc"
+
+    node_population = SimpleNamespace(
+        config={"alternate_morphologies": {"neurolucida-asc": "/data/asc"}},
+        get=lambda _: {"morphology": "cell_42"},
+        morph=SimpleNamespace(get_filepath=_get_filepath),
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    result = SonataCircuitAccess.morph_filepath(access, CellId("popA", 42))
+
+    assert result == "/data/swc/cell.swc"
+
+
+def test_morph_filepath_raises_informative_error_on_full_failure():
+    access = object.__new__(SonataCircuitAccess)
+    node_population = SimpleNamespace(
+        config={},
+        get=lambda _: {"morphology": "cell_42"},
+        morph=SimpleNamespace(
+            get_filepath=lambda *_args, **_kwargs: (_ for _ in ()).throw(BluepySnapError("missing"))
+        ),
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    with pytest.raises(BluepySnapError, match="Could not determine morphology path"):
+        SonataCircuitAccess.morph_filepath(access, CellId("popA", 42))
+
+
+def test_emodel_path_fallback_from_model_template():
+    access = object.__new__(SonataCircuitAccess)
+
+    class _Models:
+        @staticmethod
+        def get_filepath(_cell_id):
+            raise BluepySnapError("missing models helper")
+
+    node_population = SimpleNamespace(
+        models=_Models(),
+        get=lambda _: {"model_template": "hoc:MyTemplate"},
+        config={"biophysical_neuron_models_dir": "/data/emodels"},
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    result = SonataCircuitAccess.emodel_path(access, CellId("popA", 1))
+
+    assert result == "/data/emodels/MyTemplate.hoc"
+
+
+def test_emodel_path_raises_on_fallback_failure():
+    access = object.__new__(SonataCircuitAccess)
+
+    class _Models:
+        @staticmethod
+        def get_filepath(_cell_id):
+            raise BluepySnapError("missing models helper")
+
+    node_population = SimpleNamespace(
+        models=_Models(),
+        get=lambda _: {"model_template": "not_hoc_format"},
+        config={},
+    )
+    access._circuit = SimpleNamespace(nodes={"popA": node_population})
+
+    with pytest.raises(BluepySnapError, match="Could not determine emodel path"):
+        SonataCircuitAccess.emodel_path(access, CellId("popA", 1))
diff --git a/tests/test_h5_morphology_integration.py b/tests/test_h5_morphology_integration.py
new file mode 100644
index 00000000..5e08a26f
--- /dev/null
+++ b/tests/test_h5_morphology_integration.py
@@ -0,0 +1,340 @@
+#!/usr/bin/env python3
+"""H5 morphology integration tests with proper folder structure"""
+
+from pathlib import Path
+import contextlib
+
+import h5py
+import neuron
+
+from bluecellulab import Cell
+from bluecellulab.circuit.circuit_access import EmodelProperties
+from bluecellulab.cell.morphio_wrapper import MorphIOWrapper, split_morphology_path
+
+
+class TestH5MorphologyIntegration:
+    """Test H5 morphology integration with Cell constructor."""
+
+    def test_is_valid_morphology_path_direct(self):
+        """Test _is_valid_morphology_path method directly."""
+        # Import the method directly to test it without constructor
+        from bluecellulab.cell.template import NeuronTemplate
+        import tempfile
+        import os
+
+        # Create a temporary hoc template for testing
+        with tempfile.NamedTemporaryFile(suffix=".hoc", delete=False) as tmp:
+            tmp.write(b"begintemplate Dummy_Template\npublic init()\nproc init() {\n}\nendtemplate Dummy_Template")
+            tmp_path = tmp.name
+
+        with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as h5_tmp:
+            h5_path = h5_tmp.name
+        with h5py.File(h5_path, "w") as h5f:
+            h5f.create_group("some_cell")
+
+        try:
+            # Create a template instance
+            template = NeuronTemplate(tmp_path, tmp_path, "v5", None)
+
+            # Test with existing regular file
+            assert template._is_valid_morphology_path(tmp_path)
+
+            # Test with non-existent file
+            assert not template._is_valid_morphology_path("nonexistent_file.h5")
+
+            # Test with non-existent H5 container path
+            assert not template._is_valid_morphology_path("nonexistent_container.h5/cell_name")
+
+            # Test with existing H5 container
+            valid_path = f"{h5_path}/some_cell"
+            assert template._is_valid_morphology_path(valid_path)
+
+        finally:
+            # Clean up temporary file
+            os.unlink(tmp_path)
+            os.unlink(h5_path)
+
+    def test_is_valid_morphology_path_invalid_h5_container(self):
+        """Test _is_valid_morphology_path with corrupted H5 container."""
+        from bluecellulab.cell.template import NeuronTemplate
+        import tempfile
+        import os
+
+        # Create a temporary file for testing
+        with tempfile.NamedTemporaryFile(suffix=".hoc", delete=False) as tmp:
+            tmp.write(b"begintemplate Dummy_Template\npublic init()\nproc init() {\n}\nendtemplate Dummy_Template")
+            tmp_path = tmp.name
+
+        # Create a corrupted H5 file
+        with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as h5_tmp:
+            h5_tmp.write(b"corrupted data")
+            h5_path = h5_tmp.name
+
+        try:
+            template = NeuronTemplate(tmp_path, tmp_path, "v5", None)
+
+            # Test with corrupted H5 container - should return False due to exception
+            assert not template._is_valid_morphology_path(f"{h5_path}/cell_name")
+
+        finally:
+            os.unlink(tmp_path)
+            os.unlink(h5_path)
+
+    @classmethod
+    def setup_class(cls):
+        """Load compiled mechanisms once for all tests."""
+        # Load mechanisms from tests/arm64 (or x86_64)
+        mech_dir = Path(__file__).parent / "arm64"
+        if not mech_dir.exists():
+            mech_dir = Path(__file__).parent / "x86_64"
+        if mech_dir.exists():
+            with contextlib.suppress(RuntimeError):
+                neuron.load_mechanisms(str(mech_dir.parent))
+
+    def test_morphio_wrapper_single_h5(self):
+        """Test MorphIOWrapper with single H5 file."""
+        h5_file = (Path(__file__).parent / "examples" /
+                   "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies/h5" /
+                   "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5")
+
+        assert h5_file.exists(), f"H5 file not found: {h5_file}"
+
+        wrapper = MorphIOWrapper(str(h5_file))
+
+        assert wrapper._morph_ext == ".h5"
+        assert wrapper._morph_name == "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0"
+        assert len(wrapper._section_names) > 0
+        assert len(wrapper.morph_as_hoc()) > 0
+
+        print("MorphIOWrapper test passed:")
+        print(f"- Sections: {len(wrapper._section_names)}")
+        print(f"- HOC commands: {len(wrapper.morph_as_hoc())}")
+
+    def test_morphio_wrapper_h5_container(self):
+        """Test MorphIOWrapper with H5 container."""
+        container_path = (Path(__file__).parent / "examples" /
+                          "container_nbS1-O1__202247__cADpyr__L5_TPC_A" /
+                          "morphologies" / "merged-morphologies.h5")
+
+        assert container_path.exists(), f"Container file not found: {container_path}"
+
+        # Test that we can access the container file
+        # Note: Skip full MorphIOWrapper test due to long cell name issues
+        # The single H5 file test covers MorphIOWrapper functionality
+        with h5py.File(container_path, 'r') as f:
+            assert len(f.keys()) > 0, "Container should have cells"
+            print("Container test passed:")
+            print(f"- Cells available: {len(f.keys())}")
+
+    def test_split_morphology_path_single_file(self):
+        """Test split_morphology_path with single H5 file."""
+        h5_file = (Path(__file__).parent / "examples" /
+                   "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies/h5" /
+                   "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5")
+
+        collection_dir, morph_name, morph_ext = split_morphology_path(str(h5_file))
+
+        assert morph_name == "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0"
+        assert morph_ext == ".h5"
+        assert "container_nbS1-O1__202247__cADpyr__L5_TPC_A" in collection_dir
+
+    def test_split_morphology_path_container(self):
+        """Test split_morphology_path with H5 container."""
+        container_file = (
+            Path(__file__).parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies" /
+            "merged-morphologies.h5"
+        )
+
+        cell_path = (
+            f"{container_file}/"
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0"
+        )
+        collection_dir, morph_name, morph_ext = split_morphology_path(cell_path)
+
+        # The function splits based on os.path.splitext, which may truncate long names
+        assert "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA" in morph_name
+        assert str(container_file) == collection_dir
+
+    def test_morphio_wrapper_generates_valid_hoc(self):
+        """Test that MorphIOWrapper generates valid HOC commands."""
+        h5_file = (
+            Path(__file__).parent / "examples" /
+            "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies/h5/" /
+            "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5"
+        )
+
+        wrapper = MorphIOWrapper(str(h5_file))
+        commands = wrapper.morph_as_hoc()
+
+        # Verify commands are valid HOC
+        assert len(commands) > 0
+        assert any("create" in cmd for cmd in commands)
+        assert any("pt3dadd" in cmd for cmd in commands)
+        assert any("connect" in cmd for cmd in commands)
+
+        print("HOC generation test passed:")
+        print(f"Total commands: {len(commands)}")
+        print("Sample commands:")
+        for cmd in commands[:5]:
+            print(cmd)
+
+    def test_morphio_wrapper_with_neuron(self):
+        """Test that MorphIOWrapper HOC commands execute in NEURON."""
+        h5_file = (Path(__file__).parent / "examples" /
+                   "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies/h5" /
+                   "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5")
+
+        neuron.h("create __bcl_cov_tmp_sec")
+        # Clear any existing sections
+        for sec in neuron.h.allsec():
+            neuron.h.delete_section(sec=sec)
+
+        wrapper = MorphIOWrapper(str(h5_file))
+        commands = wrapper.morph_as_hoc()
+
+        # Execute HOC commands
+        for cmd in commands:
+            neuron.h(cmd)
+
+        # Verify sections were created
+        section_count = len(list(neuron.h.allsec()))
+        assert section_count > 0, "No sections created"
+
+        print("NEURON execution test passed:")
+        print(f"Sections created: {section_count}")
+
+        # Show some section names
+        print("Sample sections:")
+        for i, sec in enumerate(list(neuron.h.allsec())[:5]):
+            print(f"{sec.name()}")
+
+    def test_cell_initialization_with_h5_morphology(self):
+        """Test Cell initialization with single H5 morphology."""
+
+        h5_file = (Path(__file__).parent / "examples" /
+                   "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "morphologies/h5" /
+                   "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0.h5")
+
+        assert h5_file.exists(), f"H5 file not found: {h5_file}"
+
+        # Create cell with single H5 morphology
+        cell = Cell(
+            template_path=str(Path(__file__).parent / "examples" / "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "emodels_hoc" / "cADpyr_L5TPC.hoc"),
+            morphology_path=str(h5_file),
+            template_format="v6",
+            emodel_properties=EmodelProperties(
+                threshold_current=0.24553125,
+                holding_current=-0.09796987224035547,
+                AIS_scaler=1.0
+            )
+        )
+
+        # Verify cell was created successfully
+        assert cell.cell is not None
+        assert len(list(cell.cell.all)) > 0, "Cell should have sections"
+
+        print("Cell initialization with single H5 morphology passed")
+
+    def test_cell_initialization_with_h5_container(self):
+        """Test Cell initialization with H5 container morphology."""
+
+        container_path = (Path(__file__).parent / "examples" /
+                          "container_nbS1-O1__202247__cADpyr__L5_TPC_A" /
+                          "morphologies" / "merged-morphologies.h5")
+        cell_name = "dend-rat_20150119_LH1_cell1_axon-rp111203_C3_idA_-_Scale_x1.000_y0.950_z1.000_-_Clone_0"
+
+        assert container_path.exists(), f"Container file not found: {container_path}"
+
+        # Create cell with H5 container morphology
+        cell = Cell(
+            template_path=str(Path(__file__).parent / "examples" / "container_nbS1-O1__202247__cADpyr__L5_TPC_A" / "emodels_hoc" / "cADpyr_L5TPC.hoc"),
+            morphology_path=f"{container_path}/{cell_name}",
+            template_format="v6",
+            emodel_properties=EmodelProperties(
+                threshold_current=0.24553125,
+                holding_current=-0.09796987224035547,
+                AIS_scaler=1.0
+            )
+        )
+
+        # Verify cell was created successfully
+        assert cell.cell is not None
+        assert len(list(cell.cell.all)) > 0, "Cell should have sections"
+
+        print("Cell initialization with H5 container morphology passed")
+
+    def test_is_valid_morphology_path_with_nested_h5(self):
+        """Test _is_valid_morphology_path with nested H5 paths."""
+        from bluecellulab.cell.template import NeuronTemplate
+        import tempfile
+        import os
+
+        with tempfile.NamedTemporaryFile(suffix=".hoc", delete=False) as tmp:
+            tmp.write(b"begintemplate Dummy_Template\npublic init()\nproc init() {\n}\nendtemplate Dummy_Template")
+            tmp_path = tmp.name
+
+        with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as h5_tmp:
+            h5_path = h5_tmp.name
+        with h5py.File(h5_path, "w") as h5f:
+            h5f.create_group("nested")
+            h5f["nested"].create_group("cell")
+
+        try:
+            template = NeuronTemplate(tmp_path, tmp_path, "v5", None)
+
+            nested_path = f"{h5_path}/nested/cell"
+            assert template._is_valid_morphology_path(nested_path)
+
+        finally:
+            os.unlink(tmp_path)
+            os.unlink(h5_path)
+
+    def test_split_morphology_path_with_nested_directories(self, tmp_path):
+        """Test split_morphology_path with deeply nested directory structure."""
+        from bluecellulab.cell.morphio_wrapper import split_morphology_path
+
+        container_file = tmp_path / "deep" / "nested" / "merged-morphologies.h5"
+        container_file.parent.mkdir(parents=True)
+        container_file.write_bytes(b"dummy")
+
+        cell_path = f"{container_file}/nested/cell"
+        collection_dir, morph_name, morph_ext = split_morphology_path(cell_path)
+
+        assert str(container_file) == collection_dir
+        assert morph_name == "nested/cell"
+        assert morph_ext == ""
+
+
+if __name__ == "__main__":  # pragma: no cover
+    print("Running H5 Morphology Integration Tests")
+    print("=" * 60)
+
+    # Run tests
+    test = TestH5MorphologyIntegration()
+    test.setup_class()
+
+    try:
+        test.test_morphio_wrapper_single_h5()
+        test.test_morphio_wrapper_h5_container()
+        test.test_split_morphology_path_single_file()
+        test.test_split_morphology_path_container()
+        test.test_morphio_wrapper_generates_valid_hoc()
+        test.test_morphio_wrapper_with_neuron()
+        test.test_cell_initialization_with_h5_morphology()
+        test.test_cell_initialization_with_h5_container()
+
+        print("\n" + "=" * 60)
+        print("All H5 morphology integration tests passed!")
+        print("\nSummary:")
+        print("MorphIOWrapper loads H5 files correctly")
+        print("MorphIOWrapper handles H5 containers correctly")
+        print("split_morphology_path works for both single files and containers")
+        print("Generated HOC commands are valid")
+        print("HOC commands execute successfully in NEURON")
+        print("Cell initialization works with single H5 morphologies")
+        print("Cell initialization works with H5 container morphologies")
+        print("\nComplete H5 morphology support is now fully functional!")
+    except Exception as e:
+        print(f"\nTest failed: {e}")
+        raise