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

How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which...

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Autores principales: Tehrani-Saleh, Ali, Adami, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516857/
https://www.ncbi.nlm.nih.gov/pubmed/33286159
http://dx.doi.org/10.3390/e22040385
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author Tehrani-Saleh, Ali
Adami, Christoph
author_facet Tehrani-Saleh, Ali
Adami, Christoph
author_sort Tehrani-Saleh, Ali
collection PubMed
description How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which several measures are available, foremost among them the Shannon information, which is an undirected measure. Several information-theoretic notions of “directed information” have been used to successfully detect the flow of information in some systems, in particular in the neuroscience community. However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks (motion detection and sound localization). Besides counting the frequency of problematic logic gates, we also test whether transfer entropy applied to an activity time-series recorded from behaving digital brains can infer information flow, compared to a ground-truth model of direct influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer directed information when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.
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spelling pubmed-75168572020-11-09 Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing? Tehrani-Saleh, Ali Adami, Christoph Entropy (Basel) Article How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which several measures are available, foremost among them the Shannon information, which is an undirected measure. Several information-theoretic notions of “directed information” have been used to successfully detect the flow of information in some systems, in particular in the neuroscience community. However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks (motion detection and sound localization). Besides counting the frequency of problematic logic gates, we also test whether transfer entropy applied to an activity time-series recorded from behaving digital brains can infer information flow, compared to a ground-truth model of direct influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer directed information when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system. MDPI 2020-03-28 /pmc/articles/PMC7516857/ /pubmed/33286159 http://dx.doi.org/10.3390/e22040385 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tehrani-Saleh, Ali
Adami, Christoph
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title_full Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title_fullStr Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title_full_unstemmed Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title_short Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
title_sort can transfer entropy infer information flow in neuronal circuits for cognitive processing?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516857/
https://www.ncbi.nlm.nih.gov/pubmed/33286159
http://dx.doi.org/10.3390/e22040385
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