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001     889321
005     20240313094932.0
037 _ _ |a FZJ-2021-00213
100 1 _ |a Helias, Moritz
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111 2 _ |c Heidelberg
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245 _ _ |a Correlations, chaos, and criticality in neural networks
|f 2020-11-06 -
260 _ _ |c 2020
336 7 _ |a Conference Paper
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336 7 _ |a Other
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520 _ _ |a Correlations, chaos, and criticality in neural networksMoritz HeliasINM-6 Juelich Research CentreTheory of condensed matter physicsRWTH AachenThe remarkable properties of information-processing of biological andof artificial neuronal networks alike arise from the interaction oflarge numbers of neurons. A central quest is thus to characterizetheir collective states. The directed coupling between pairs ofneurons and their continuous dissipation of energy, moreover, causedynamics of neuronal networks outside thermodynamic equilibrium.Tools from non-equilibrium statistical mechanics and field theory arethus instrumental to obtain a quantitative understanding. We herepresent progress with this recent approach [1].On the experimental side, we show how correlations betweenpairs of neurons are informative on the dynamics ofcortical networks: they are poised near a transition to chaos [2].Close to this transition, we find prolongued sequential memoryfor past signals [3]. In the chaotic regime, networks offerrepresentations of information whose dimensionality expands with time.We show how this mechanism aids classification performance [3].Together these works illustrate the fruitful interplay betweentheoretical physics, neuronal networks, and neural informationprocessing.1. Helias, Dahmen (2020) Statistical field theory for neural networks.Springer lecture notes in physics.2. Dahmen D, Grün S, Diesmann M, Helias M (2019). Second type of criticality in the brain uncovers rich multiple-neuron dynamics. PNAS 116 (26) 13051-130603. Schuecker J, Goedeke S, Helias M (2018). Optimal sequence memory in driven random networks. Phys Rev X 8, 0410294. Keup, Kuehn, Dahmen, Helias (2020) Transient chaotic dimensionality expansion by recurrent networks. arXiv:2002.11006 [cond-mat.dis-nn]
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