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rescience_metadata.yaml
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rescience_metadata.yaml
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# To be filled by the author(s) at the time of submission
# -------------------------------------------------------
# Title of the article:
# - For a successful replication, it shoudl be prefixed with "[Re]"
# - For a failed replication, it should be prefixed with "[¬Re]"
# - For other article types, no instruction (but please, not too long)
title: "[Re] Connectivity reflects coding a model of voltage-based STDP with
homeostasis"
# List of authors with name, orcid number, email and affiliation
# Affiliation "*" means contact author
authors:
- name: Rene Larisch
orcid:
email: [email protected]
affiliations: 1,*
# List of affiliations with code (corresponding to author affiliations), name
# and address. You can also use these affiliations to add text such as "Equal
# contributions" as name (with no address).
affiliations:
- code: 1
name: Professorship for Artificial Intelligence, Department of Computer Science, Chemnitz University of Technology
address: D-09107 Chemnitz, Germany
# List of keywords (adding the programming language might be a good idea)
keywords: Spike Timing-Dependent Plasticity (STDP), Computational Neuroscience, Unsupervised Learning, Python
# Code URL and DOI (url is mandatory for replication, doi after acceptance)
# You can get a DOI for your code from Zenodo,
# see https://guides.github.com/activities/citable-code/
code:
- url: https://github.com/rLarisch/ReScience-submission/tree/Larisch-2019/code
- doi:
# Date URL and DOI (optional if no data)
data:
- url:
- doi:
# Information about the original article that has been replicated
replication:
- cite: Connectivity reflects coding a model of voltage-based STDP with homeostasis,
C.Clopath, L. Buesing, E.Vasilaki and W. Gerstner, Nature Neuroscience 13.3 (2010), pp. 344-352 # Full textual citation
- bib: # Bibtex key (if any) in your bibliography file
- url: https://www.nature.com/articles/nn.2479 # URL to the PDF, try to link to a non-paywall version
- doi: 10.1038/nn.2479 # Regular digital object identifier
# Don't forget to surround abstract with double quotes
abstract: "Since the first description of spike timing-dependent plasticity (STDP), different models
of STDP have been published to reproduce different experimental findings.
Clopath et al. (2010) introduced a STDP model which is able to reproduce the experimental findings of triplet studies.
They implemented a homeostatic mechanism to control the level of generated LTD, based on the relationship between the average postsynaptic membrane
potential and a reference value. With this voltage-based STDP rule thei reproduce a wide range of physiological experiments.
We here present a reimplementation of the Clopath et al . (2010) learning rule in Python with the help of the neuro-simulator ANNarchy."
# Bibliography file (yours)
bibliography: bibliography.bib
# Type of the article
# Type can be:
# * Editorial
# * Letter
# * Replication
type: Replication
# Scientific domain of the article (e.g. Computational Neuroscience)
# (one domain only & try to be not overly specific)
domain: Computational Neuroscience
# Coding language (main one only if several)
language: Python
# To be filled by the author(s) after acceptance
# -----------------------------------------------------------------------------
# For example, the URL of the GitHub issue where review actually occured
review:
- url: https://github.com/ReScience/ReScience-submission/pull/57#issuecomment-549728501
contributors:
- name: Georgios Detorakis
orcid: 0000-0001-5891-1702
role: editor
- name: Christian Jarvers
orcid: 0000-0003-0062-9776
role: reviewer
- name: Andrew P. Davison
orcid: 0000-0002-4793-7541
role: reviewer
# This information will be provided by the editor
dates:
- received: November 1, 2018
- accepted: November 4, 2019
- published: November 12, 2019
# This information will be provided by the editor
article:
- number: 2
- doi: 10.5281/zenodo.3538217
- url: https://zenodo.org/record/3538217/files/article.pdf
# This information will be provided by the editor
journal:
- name: "ReScience C"
- issn: 2430-3658
- volume: 5
- issue: 3