Coarse--graining, fixed points, and scaling in a large population of neurons

TL;DR

This paper introduces a coarse-graining method for neural activity in large hippocampal networks, revealing fixed points and scaling behaviors indicative of complex collective dynamics.

Contribution

It presents a novel phenomenological coarse-graining approach applied to large neural populations, uncovering non-Gaussian fixed points and scaling phenomena in neural activity.

Findings

Distributions of coarse-grained variables approach a non-Gaussian fixed point

Evidence of scaling in static and dynamic neural activity measures

Collective neural behavior characterized by a non-trivial fixed point

Abstract

We develop a phenomenological coarse--graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse--grained variables seem to approach a fixed non--Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a non--trivial fixed point.