Amanda Minisi

Jan 10, 2025 | biorxiv.org | Marius Pachitariu |Lin Zhong |Alexa Gracias |Amanda Minisi

AbstractArtificial neural networks learn faster if they are initialized well. Good initializations can generate high-dimensional macroscopic dynamics with long timescales. It is not known if biological neural networks have similar properties. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice (two-photon and electrophysiology) are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized.