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 192
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
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Keywords
- statistical learning
- neural networks
- neural coding
- math of deep learning
- multivariate probabilistic modeling
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