English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To infer information flow in any network of agents, it is important first andforemost to establish causal temporal relations between the nodes. Practicaland automated methods that can infer causality are difficult to find, and thesubject of ongoing research. While Shannon information only detectscorrelation, there are several information-theoretic notions of "directedinformation" that have successfully detected causality in some systems, inparticular in the neuroscience community. However, recent work has shown thatsome directed information measures can sometimes inadequately estimate theextent of causal relations, or even fail to identify existing cause-effectrelations between components of systems, especially if neurons contribute in acryptographic manner to influence the effector neuron. Here, we test how oftencryptographic logic emerges in an evolutionary process that generatesartificial neural circuits for two fundamental cognitive tasks: motiondetection and sound localization. We also test whether activity time-seriesrecorded from behaving digital brains can infer information flow using thetransfer entropy concept, when compared to a ground-truth model of causalinfluence constructed from connectivity and circuit logic. Our results suggestthat transfer entropy will sometimes fail to infer causality when it exists,and sometimes suggest a causal connection when there is none. However, theextent of incorrect inference strongly depends on the cognitive taskconsidered. These results emphasize the importance of understanding thefundamental logic processes that contribute to information flow in cognitiveprocessing, and quantifying their relevance in any given nervous system.

Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?