A critical initialization for biological neural networks
type: NewsArticle
description: Large-scale neural activity in mice, except in the hippocampal area CA1, exhibits dynamics similar to those of a linear system governed by a critically normalized random symmetric matrix. This critical initialization produces high-dimensional, persistent activity modes useful for tasks like working memory, and the findings suggest that such initialization principles could improve artificial neural network design.
date published: 2026-05-20T00:00:00+00:00
date modified: 2026-05-20T23:08:09+00:00
language: en
is accessible for free: true
article body: ## Abstract Intrinsically generated, brainwide neural activity displays macroscopic coordination among large populations of neurons that persists beyond the biophysical timescales of individual neurons 1,2,3. It is not well understood how these macroscopic behaviours arise from microscopic, short-lived interactions between pairs of neurons. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was population activity in hippocampal area CA1, which resembled an efficient, uncorrelated neural code that may be optimized for information storage capacity. High-dimensional, global activity modes emerged in critically normalized artificial networks and persisted under sparse, clustered or spatial connectivity. These dynamics were useful for solving time-dependent tasks such as a zero-shot working memory task. ### Similar content being viewed by others ## Main Intrinsically generated neuronal activity contains macroscopic modes of coordination between neurons that extend across the entire mouse brain 2,3,4,5. More activity variance is concentrated into the top dimensions of neural activity than would be expected for independently firing neurons. At the same time, there is no low-dimensional cutoff of variance concentration, and variance scales as a power-law of the eigenmode number . As yet, there are no mechanistic models that can explain this scaling of variance, but macroscopic variability in general has been hypothesized to arise from neural network dynamics operating in either a critical or chaotic regime [2](/articles/s41586-026-10528-1#ref-CR2). [6](#ref-CR6),[7](#ref-CR7),[8](#ref-CR8),[9](#ref-CR9),[10](#ref-CR10),[11](/articles/s41586-026-10528-1#ref-CR11)Emergent macroscopic structure has also been studied in artificial neural networks, usually in the context of neural network init…
publisher: Web Pulse
keywords: research, science
citation: CreativeWork