美国HHMI珍妮莉亚研究园区Carsen Stringer团队揭示了生物神经网络的关键初始化。相关论文发表在2026年5月20日出版的《自然》杂志上。

本研究表明，小鼠大规模神经记录的特征值谱和动态特性与由严格归一化的随机对称矩阵控制的线性动力学产生的特征值谱和动态特性相似。一个例外是海马区CA1的群体活动，它类似于一个有效的、不相关的神经代码，可能会优化信息存储容量。高维的全球活动模式出现在严格归一化的人工网络中，并在稀疏、群集或空间连通性下持续存在。这些动态对解决时间依赖性任务（如零差工作记忆任务）很有帮助。

据介绍，内在生成的全脑神经活动显示了大量神经元之间的宏观协调，这种协调持续存在于单个神经元的生物物理时间尺度之外。目前还不清楚这些宏观行为是如何从成对神经元之间微观的、短暂的相互作用中产生的。

附：英文原文

Title: A critical initialization for biological neural networks

Author: Pachitariu, Marius, Zhong, Lin, Gracias, Alexa, Minisi, Amanda, Lopez, Crystall, Stringer, Carsen

Issue&Volume: 2026-05-20

Abstract: Intrinsically generated, brainwide neural activity displays macroscopic coordination among large populations of neurons that persists beyond the biophysical timescales of individual neurons1,2,3. 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.

DOI: 10.1038/s41586-026-10528-1

Source: https://www.nature.com/articles/s41586-026-10528-1