Discovering the Computational Relevance of Brain Network Organization

TL;DR

This paper reviews recent advances in linking brain network organization with cognitive processing, proposing network coding models that use connectivity to predict neural information transfer and cognitive representations.

Contribution

It introduces a new framework called network coding models that connect empirical brain connectivity with cognitive information flow, advancing understanding of neural computation.

Findings

Network coding models successfully predict cognitive representations.

Connectivity patterns mechanistically underpin neural functions.

Empirical data supports distributed activity flow as a basis for cognition.

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.