Coherent chaos in a recurrent neural network with structured connectivity

We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks,...

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Veröffentlicht in:	PLoS computational biology Jg. 14; H. 12; S. e1006309
Hauptverfasser:	Landau, Itamar Daniel, Sompolinsky, Haim
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.12.2018 Public Library of Science (PLoS)
Schlagworte:	Biology and Life Sciences Broken symmetry Coherence Computer and Information Sciences Computer Simulation - statistics & numerical data Constraints Dynamics Eigenvalues Feedback loops Fluctuations Medicine and Health Sciences Memory Models, Neurological Neocortex Neural networks Neural Networks, Computer Neurons Neurons - physiology Neurosciences Nonlinear Dynamics Orthogonality Phase transitions Physical Sciences Physics Recurrent neural networks Research and Analysis Methods Scaling Size effects Standard deviation Switching theory Theory Jerusalem Israel Israel
ISSN:	1553-7358, 1553-734X, 1553-7358
Online-Zugang:	Volltext
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