Unveiling the intrinsic dynamics of biological and artificial neural networks: from criticality to optimal representations

TL;DR

This paper reviews recent advances in understanding neural network dynamics, providing evidence of near-critical behavior in biological brains and artificial networks, which may underpin optimal information processing and inspire neuromorphic computing.

Contribution

The authors combine experimental data analysis with theoretical tools to demonstrate near-critical dynamics in brain regions and artificial networks, advancing understanding of neural function and computation.

Findings

Evidence of quasi-universal scaling and near-criticality in brain data.

Artificial networks tuned for optimal performance exhibit near-critical dynamics.

Insights into principles guiding brain function and neuromorphic design.

Abstract

Deciphering the underpinnings of the dynamical processes leading to information transmission, processing, and storing in the brain is a crucial challenge in neuroscience. An inspiring but speculative theoretical idea is that such dynamics should operate at the brink of a phase transition, i.e., at the edge between different collective phases, to entail a rich dynamical repertoire and optimize functional capabilities. In recent years, research guided by the advent of high-throughput data and new theoretical developments has contributed to making a quantitative validation of such a hypothesis. Here we review recent advances in this field, stressing our contributions. In particular, we use data from thousands of individually recorded neurons in the mouse brain and tools such as a phenomenological renormalization group analysis, theory of disordered systems, and random matrix theory. These combined approaches provide novel evidence of quasi-universal scaling and near-critical behavior emerging in different brain regions. Moreover, we design artificial neural networks under the reservoir-computing paradigm and show that their internal dynamical states become near critical when we tune the networks for optimal performance. These results not only open new perspectives for understanding the ultimate principles guiding brain function but also towards the development of brain-inspired, neuromorphic computation.