Discovering the Computational Relevance of Brain Network Organization

Takuya Ito, Luke Hearne, Ravi Mill, Carrisa Cocuzza, Michael W. Cole

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)25-38
Number of pages14
JournalTrends in Cognitive Sciences
Volume24
Issue number1
DOIs
StatePublished - Jan 2020

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Brain
Automatic Data Processing
Cognition

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

Keywords

  • artificial intelligence
  • connectivity
  • connectome
  • machine learning
  • neural encoding/decoding
  • neural networks
  • representations

Cite this

Ito, Takuya ; Hearne, Luke ; Mill, Ravi ; Cocuzza, Carrisa ; Cole, Michael W. / Discovering the Computational Relevance of Brain Network Organization. In: Trends in Cognitive Sciences. 2020 ; Vol. 24, No. 1. pp. 25-38.
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Discovering the Computational Relevance of Brain Network Organization. / Ito, Takuya; Hearne, Luke; Mill, Ravi; Cocuzza, Carrisa; Cole, Michael W.

In: Trends in Cognitive Sciences, Vol. 24, No. 1, 01.2020, p. 25-38.

Research output: Contribution to journalReview article

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