Statistical Learning in Computational Neuroscience and Neural Coding
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 46
Special Issue Editor
Special Issue Information
Dear Colleagues,
While the use of information theory for understanding neural coding has a long history in neuroscience, new technical developments—enhanced tools for measuring brain activity on the one side, and advances in machine learning approaches for modeling neural-like learning systems on the other—require new statistical tool developments applicable to high-dimensional and potentially non-stationary data. Importantly, it opens up the possibility of novel theoretical use-cases, in particular in their use for understanding the properties of artificial learning systems.
This Special Issue aims to provide a forum for the presentation of new approaches for using information theory in the service of understanding principles of statistical learning at the computational level and the application of such tools to the study of the brain.
Topics of interest include, but are not limited to, the following:
- Mathematical modeling of brain computation or artificial neural systems;
- New statistical methods for neural data constructed based on information theoretic principles;
- Theoretical models of statistical learning;
- Information theoretic investigation of artificial intelligent systems;
- Neural coding.
Dr. Cristina Savin
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- statistical learning
- neural networks
- neural coding
- math of deep learning
- multivariate probabilistic modeling
