Overview
The Journal of Computational Neuroscience focuses on understanding brain function at the level of neurons and circuits via computational and model-based approaches that are tied to biology and are experimentally testable.
- Publishes full-length original papers, as well as rapid communications, perspectives, and review articles.
- Papers combining theoretical and experimental work are especially encouraged.
- Papers reporting computational and/or statistical analysis, without a model, are not appropriate for this journal.
- Papers reporting research utilizing machine learning or related methods to analyze neural data, but that do not address function, are not appropriate for this journal.
- Prospective authors who are unsure of whether their manuscript is in the scope of the Journal are encouraged to contact one of the Editors in Chief prior to submission.
This is a transformative journal, you may have access to funding.
- Editor-in-Chief
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- Alain Destexhe,
- Jonathan Victor
- Journal Impact Factor
- 1.5 (2023)
- 5-year Journal Impact Factor
- 1.7 (2023)
- Submission to first decision (median)
- 5 days
- Downloads
- 176,891 (2023)
Societies and partnerships
Latest articles
Journal updates
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The Best of Journal of Computational Neuroscience 2020-2021
We are showcasing the top articles from Journal of Computational Neuroscience, published in 2020 and 2021. This collection features the highlights of the latest academic research. We hope you enjoy your free reading!
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New Article Type “Perspective” now open for submissions
To provide a forum for authors to present new ideas, comment on published material, or re-interpret data, a new article type was set up at this journal: “Perspective”. Articles should be brief and timely, and of wide interest to the computational neuroscience community.
The manuscript should not exceed 3000 words and 1 figure or table, and it should not report any new data, but could re-analyze existing data or propose a new interpretation of published data. A fast publication is expected.
Read the Editors-in-Chief’s Editorial on “Perspectives”.
Submit your paper here.
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Editor's Highlight: A model for the transfer of control from the brain to the spinal cord through synaptic learning
In this paper, the authors present a novel approach to understanding the organization of spinal circuitry. Rather than taking the viewpoint that the circuitry is hardwired, they consider models in which spinal synaptic organization is learned from descending control signals.
Journal information
- Electronic ISSN
- 1573-6873
- Print ISSN
- 0929-5313
- Abstracted and indexed in
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- ACM Digital Library
- BFI List
- BIOSIS
- Baidu
- Biological Abstracts
- CAB Abstracts
- CLOCKSS
- CNKI
- CNPIEC
- Current Contents/Life Sciences
- DBLP
- Dimensions
- EBSCO
- EI Compendex
- EMBASE
- Google Scholar
- INSPEC
- Japanese Science and Technology Agency (JST)
- Mathematical Reviews
- Medline
- Meta
- Naver
- OCLC WorldCat Discovery Service
- Portico
- ProQuest
- Reaxys
- SCImago
- SCOPUS
- Science Citation Index Expanded (SCIE)
- TD Net Discovery Service
- UGC-CARE List (India)
- Wanfang
- zbMATH
- Copyright information
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