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Research articles, review articles as well as short communications are invited. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Manuscripts can be submitted until the deadline. Manuscripts should be submitted online at

Relationships between artificial and natural neural systems. Measurement of information flow in multimodal neural datasets Models of information flow in clinical neuroscientific settings Ĭonnections between information flow and causality

Scalability of information flow methods in high dimensional neural systems Īnalysis and extensions of established information flow measures This Special Issue will assimilate the current approaches to the following (and related) topics:Īxiomatic definitions, measures and/or estimators of information flow in neural systems The Special Issue solicits papers that are, in their essence, intellectual and/or theoretical, although demonstration on real or synthetic datasets is encouraged when possible. The issue is intended to have a balanced representation between natural and artificial worlds, and papers connecting the two, or critiquing the perceived connection between the two, are also of interest. Information theory here is to be interpreted broadly, including, for instance, classical (Shannon) information theory, algorithmic information theory, control theory, and integrated information theory. This Special Issue focuses on core information theoretic issues pertaining to flows of information in natural and artificial neural networks. Many of these issues require careful and rigorous approaches that have only recently begun being developed. Societal implications of defining, understanding, designing, and/or affecting information flows are deep and broad, influencing all aspects of our lives. While the term information flow is used frequently in practical contexts, such as clinical neuroscience or the optimization/interpretation of artificial neural networks, fundamental exploration of the topic has received limited attention. In an exciting confluence, information flows in neural networks-both artificial and natural-are garnering immense interest.