[Paper Review] Brain network organization as the computational architecture of cognition

Takuya Ito, Luke J. Hearne+3 more|arXiv (Cornell University)|Jul 8, 2019

Neural dynamics and brain function110 references1 citations

TL;DR

The paper proposes a network coding framework that explains how brain connectivity patterns computationally shape cognitive representations through distributed neural activity flows. By modeling information transfer via connectivity, the framework successfully predicts face selectivity in the fusiform face area, demonstrating that localized functions emerge from connectivity-specified dynamics.

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 (e.g., face selectivity in the fusiform face area). 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.

Motivation & Objective

• To understand how cognitive functions emerge from distributed neural activity rather than localized processing alone.
• To bridge empirical brain network organization with cognitive information processing mechanisms.
• To develop a computational framework that explains how connectivity patterns specify neural computation.
• To test whether connectivity alone can predict the formation of cognitive representations like face selectivity.

Proposed method

• Develops network coding models that simulate information transfer through neural activity flows specified by brain connectivity patterns.
• Uses empirical and simulated brain network data to map how connectivity shapes the emergence of cognitive representations.
• Applies encoding/decoding models to predict functional specialization in regions such as the fusiform face area.
• Relies on connectivity as the primary determinant of information flow dynamics in distributed neural systems.
• Validates predictions by comparing model outputs with observed neural responses to stimuli like faces.

Experimental results

Research questions

• RQ1 How do connectivity patterns in the brain give rise to specific cognitive representations?
• RQ2 Can distributed activity flows, governed by connectivity, explain the emergence of localized neural functions?
• RQ3 To what extent can network coding models predict face selectivity in the fusiform face area?
• RQ4 How does brain network architecture constrain or enable cognitive computation?

Key findings

• Network coding models successfully predict face selectivity in the fusiform face area based solely on connectivity patterns.
• The formation of cognitive representations is explained by information flow processes specified by connectivity, not by localized computation alone.
• Distributed neural activity flows, guided by connectivity, can generate functionally specialized regions.
• The framework supports the hypothesis that localized neural functions are computed through connectivity-specified dynamics.