Sparse Edge Encoder (SEE): I. Visual recognition in neuronal networks

TL;DR

This study simulates neuronal networks processing visual information, revealing that optimal visual recognition does not necessarily occur at the critical state, challenging the criticality hypothesis in brain function.

Contribution

It demonstrates through simulation that optimal visual recognition in neuronal networks does not require the network to be at criticality, providing new insights into brain information processing.

Findings

Optimal recognition state is near but not at criticality.

Mutual information and dynamical range are maximized near, but not at, criticality.

Results are consistent across different images and network sizes.

Abstract

In the past few decades, there have been intense debates whether the brain operates at a critical state. To verify the criticality hypothesis in the neuronal networks is challenging and the accumulating experimental and theoretical results remain controversial at this point. Here we simulate how visual information of a nature image is processed by the finite Kinouchi-Copelli neuronal network, extracting the trends of the mutual information (how sensible the neuronal network is), the dynamical range (how sensitive the network responds to external stimuli) and the statistical fluctuations (how criticality is defined in conventional statistical physics). It is rather remarkable that the optimized state for visual recognition, although close to, does not coincide with the critical state where the statistical fluctuations reach the maximum. Different images and/or network sizes of course lead to differences in details but the trend of the information optimization remains the same. Our findings pave the first step to investigate how the information processing is optimized in different neuronal networks and suggest that the criticality hypothesis may not be necessary to explain why a neuronal network can process information smartly.