Literature DB >> 31727507

Discovering the Computational Relevance of Brain Network Organization.

Takuya Ito1, Luke Hearne2, Ravi Mill2, Carrisa Cocuzza1, Michael W Cole3.   

Abstract

Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  artificial intelligence; connectivity; connectome; machine learning; neural encoding/decoding; neural networks; representations

Mesh:

Year:  2019        PMID: 31727507      PMCID: PMC6943194          DOI: 10.1016/j.tics.2019.10.005

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


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  11 in total

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