Internal dynamics of recurrent neural networks trained to generate complex spatiotemporal patterns

Oleg V. Maslennikov, Chao Gao, Vladimir I. Nekorkin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

How complex patterns generated by neural systems are represented in individual neuronal activity is an essential problem in computational neuroscience as well as machine learning communities. Here, based on recurrent neural networks in the form of feedback reservoir computers, we show microscopic features resulting in generating spatiotemporal patterns including multicluster and chimera states. We show the effect of individual neural trajectories as well as whole-network activity distributions on exhibiting particular regimes. In addition, we address the question how trained output weights contribute to the autonomous multidimensional dynamics.

Original languageEnglish
Article number093125
JournalChaos
Volume33
Issue number9
DOIs
StatePublished - 1 Sep 2023

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