Are cortical microcircuits optimized for information flux? -- A simulation-based reverse engineering study

TL;DR

This study investigates whether cortical microcircuits are optimized for information flux, revealing how embedding networks enhance flux through biases and stochasticity, with implications for biological understanding and artificial system design.

Contribution

The paper demonstrates that embedding networks increase information flux in cortical microcircuits via effective biases and stochastic fluctuations, and shows how optimized biases can further enhance flux.

Findings

Embedding networks significantly boost information flux in core cortical models.

Effective biases and stochastic fluctuations prevent the network from trapping in simple attractors.

Optimized biases can be self-organized to maximize information flux.

Abstract

A sufficiently large information flux in recurrent neural networks, quantified by the mutual information between successive network states, is considered a prerequisite for rich information processing capabilities. This raises the question of whether biological neural networks, such as cortical microcolumns, may be structurally organized to enhance information flux. To investigate this possibility, we study a simplified model of the cortical layer 5 architecture, in which a densely and strongly interconnected core population is embedded within a larger supporting network. Surprisingly, we find that the embedding network exerts a pronounced flux-enhancing effect on the core dynamics. Systematic reverse-engineering analyses reveal that the embedding network provides two key contributions: first, it generates effective biases that shift core neurons into a higher-entropy operating regime; second, it supplies stochastic fluctuations that prevent the network from becoming trapped in simple fixed-point or oscillatory attractors through the mechanism of Recurrence Resonance. We further show that the information flux can be increased even beyond the biologically embedded case by applying individually optimized biases to the core neurons, and that these biases can emerge from a simple self-organization principle. Our findings are relevant both for the functional interpretation of biological neural circuits and for the design of artificial recurrent systems such as reservoir computers.