Discovering the Computational Relevance of Brain Network Organization

Trends Cogn Sci. 2020 Jan;24(1):25-38. doi: 10.1016/j.tics.2019.10.005. Epub 2019 Nov 11.

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.

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

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Brain*
  • Cognition*
  • Humans
  • Neural Pathways