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Updated: Jan 3, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Discovering the Computational Relevance of Brain Network Organization
Takuya Ito1, Luke Hearne2, Ravi Mill2
1Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ 07102, USA.
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.
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