Dynamical criticality in the collective activity of a population of retinal neurons

TL;DR

This paper introduces a new method to assess criticality in neural networks, demonstrating that retinal ganglion cell activity operates near a critical point and emphasizing the importance of considering temporal dynamics for accurate analysis.

Contribution

A novel approach to evaluate criticality that incorporates dynamical activity, providing stronger evidence for criticality in neural populations compared to previous methods.

Findings

Retinal ganglion cell activity is near a critical point.

Temporal dynamics significantly improve criticality detection.

The method is applicable to other biological networks.

Abstract

Recent experimental results based on multi-electrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and disorder. However, evidence for criticality has relied on the definition of arbitrary order parameters, or on models that do not address the dynamical nature of network activity. Here we introduce a novel approach to assess criticality that overcomes these limitations, while encompassing and generalizing previous criteria. We find a simple model to describe the global activity of large populations of ganglion cells in the rat retina, and show that their statistics are poised near a critical point. Taking into account the temporal dynamics of the activity greatly enhances the evidence for criticality, revealing it where previous methods would not. The approach is general and could be used in other biological networks.