Identification of recurrent dynamics in distributed neural populations.

ABSTRACT: Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. Recently, recurrent switching dynamical systems have been used to tackle the scale and complexity of these data. However, an important challenge remains in providing insights into the existence and structure of recurrent linear dynamics in neural time series data. Here we test a scalable approach to time-varying autoregression with low-rank tensors to recover the recurrent dynamics in stochastic neural mass models with multiple stable attractors. We demonstrate that the parsimonious representation of time-varying system matrices in terms of temporal modes can recover the attractor structure of simple systems via cluster

SUBMITTER: Osuna-Orozco R 

PROVIDER: S-EPMC11838891 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature