Applications of Information Theory in Neuroscience
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 6499
Special Issue Editor
Interests: statistical inference; econometrics; neuroscience; biological information processing and causality problems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The origins of Information Theory date back to Claude E. Shannon’s publication of the paper “A Mathematical Theory of Communication” in the Bell System Technical Journal in 1948. In terms of the colloquial meaning of information, Shannon’s paper deals with the carriers of information, and not with information itself. However, the importance and flexibility of Shannon’s work was quickly recognized, and many attempts were made to apply his theory in various fields outside its original scope of communications. Very soon after Shannon’s initial publication, several manuscripts were published which provide the foundations of much of the current use of Information Theory in neuroscience.
This Special Issue aims to be a forum for the presentation of novel approaches in neuroscience using Information Theory, as well as the development of new information theoretic results inspired by problems in neuroscience. Research at the interface of neuroscience, Information Theory, and other disciplines is also welcome.
Prof. Dr. Joanna Tyrcha
Guest Editor
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Keywords
- information flow
- transfer entropy
- causality measures
- multivariate statistics
- time series
- statistical inference
- neuroscience
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