Journal of Computational Neuroscience is a Transformative Journal (TJ). When research is accepted for publication, authors can choose to publish using either the traditional publishing route OR via immediate gold Open Access. (Check your funding options at https://www.springer.com/journal/10827/open-access-publishing#Fees%20and%20Funding )
Journal of Computational Neuroscience welcomes full length original papers, rapid communications, review articles, and perspective papers describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily, theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods with the potential to yield insights into the function of the nervous system, are also welcomed. It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience. However, papers that are primarily devoted to new methods or analyses should demonstrate their utility for the investigation of mechanisms or principles of neural function.
Prospective authors are strongly encouraged to consult recent issues of the Journal to be sure that their submissions are in scope. The Journal 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. Examples of work that is not within the Journal's scope include (i) presentations of signal-processing algorithms that are purely methodological or for biomedical applications such as brain-computer interfaces or seizure detection, and (ii) computational analysis of genomic data without a clear tie-in to neural mechanisms of brain function.
Before submitting your manuscript to this journal, please use the Aims & Scope text to make sure that the content fits into the journal.
Consideration of these two points is important for this purpose:
Details concerning the submission and publication procedures:
- No page charges
- Color is free
- Optional Gold Open Access: 2022 Open Access Publication Fee (APC) for this journal is £2090.00/$3190.00/€2490.00 net (see funding options)
Neuroscience Peer Review Consortium
The Journal is pleased to be a member of the Neuroscience Peer Review Consortium (NPRC), an alliance of neuroscience journals that have agreed to share manuscript reviews at the author's request.
This is a transformative journal, you may have access to funding.
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!
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.
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.