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- W4386822689 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." @default.
- W4386822689 created "2023-09-19" @default.
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- W4386822689 date "2023-09-01" @default.
- W4386822689 modified "2023-09-26" @default.
- W4386822689 title "Internal dynamics of recurrent neural networks trained to generate complex spatiotemporal patterns" @default.
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- W4386822689 doi "https://doi.org/10.1063/5.0166359" @default.
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