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Review Request : R. Larisch #57
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Sorry for the delay. Here's my review of the updates, based on commit c869eee: All of my main concerns have been addressed by the updates. Concerning replication, the control experiments (Figure 4 c and d) are now included. It is now much clearer which parts of the implementation were taken from the published Matlab code and how and why certain parameters were changed. Figure 5 and 6 are still not reproduced, but as discussed before I do support publication as a partial replication. The code is now reproducible thanks to the use of a random seed in the experiment on receptive field formation. Concerning clarity, I think the paper reads more fluently now. The supplement helps keep an overview over the parameters. Some minor issues remain, which should be easy to fix:
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Hello @gdetor and @cJarvers, Still looking forward to the comments of @apdavison. Best regards, |
@cJarvers Thank you for the review. |
I am happy with the changes, in particular running the code locally now produces identical figures to those in the manuscript. A couple of minor points:
As I mentioned in the original review, I don't think this should be described as a "reference implementation" because of the parameter changes that were needed, but I support publishing it as a partial replication. |
@apdavison Thank you for the review |
I have fixed the theta in both PDF-files and set the Python version consistent in both files. @gdetor, it would be fine for me to publish it as a "partial replication", If it is ok, I would change it in the original Request from "fully replicated" to "partially replicated". |
Hello @gdetor, |
You can explain in the introduction that this is a partial replication of ... |
@apdavison and @cJarvers Are you ok with the final draft? |
@gdetor yes |
1 similar comment
@gdetor yes |
@rLarisch Congratulations, your paper has been accepted. I started the publication process, however, I noticed that the license is missing from your home repository. Can you please update the repository? |
@gdetor A LICENCE.txt to the repository. Thank you for your help during the process. @apdavison and @cJarvers thank you both for your reviews and your constructive comments. |
Hi @rLarisch, because you use the old Rescience template you have to update the metadata on your side. I attach the corresponding metadata.yaml file that you have to use (I have filled out all the necessary information). |
Hi @gdetor, there was a metadata.yaml file in the article/new_template/ directory. I have now paste your metadata.yaml file in the article/ directory and have updated the .yaml file in the article/new_template/ directory. I hope this is ok. |
Hi, @rLarisch thank you for updating the repo, however, you have to regenerate the pdf (new template) using the metadata file I sent you since it has all the necessary information for publishing the article. Thank you. |
Hi @gdetor, I have updated the article.pdf from the new template. Should I update the corresponding informations in the *.md file from the old template too ? |
@rLarisch It would be nice to have it for consistency. Furthermore, I would like to ask you to regenerate the pdf with the following metadata file (I confused the sandbox metadata with the Zenodo one). The attached one is the final. Thank you and my apologies for the inconvenience. |
Hi @gdetor, the article.pdf with the new metadata.yaml file is updated and the corresponding information in the old *.md entered and the corresponding pdf fresh compiled. |
Hi @rLarisch, your article is now online http://rescience.github.io/bibliography/Larisch_2019.html |
AUTHOR
Dear @ReScience/editors,
I request a review for the following replication:
Original article
Title: Connectivity reflects coding: a model of voltage-based STDP with homeostasis
Author(s): Clopath, C., Büsing, L., Vasilaki, E. and Gerstner, W.
Journal (or Conference): Nature Neuroscience
Year: 2010
DOI: 10.1038/nn.2479
PDF: https://www.nature.com/articles/nn.2479
Replication
Author(s): Larisch, R.
Repository: https://github.com/rLarisch/ReScience-submission/tree/Larisch-2019
PDF: https://github.com/rLarisch/ReScience-submission/blob/Larisch-2019/article/Larisch-2019.pdf
Keywords: Spike Timing-Dependent Plasticity (STDP), Computational Neuroscience, Unsupervised Learning
Language: Python
Domain: Computational Neuroscience
Results
Potential reviewers
EDITOR