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Emergence of time persistence in an interpretable data-driven neural network model
Sebastien Wolf, Guillaume Le Goc, Simona Cocco, Georges Debrégeas, Rémi Monasson
doi: https://doi.org/10.1101/2022.02.02.478841
Sebastien Wolf
aLaboratory of Physics of the Ecole Normale Supérieure, CNRS UMR 8023 & PSL Research, Sorbonne Université, Université de Paris, Paris, France
Guillaume Le Goc
bSorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP), Paris, France
Simona Cocco
aLaboratory of Physics of the Ecole Normale Supérieure, CNRS UMR 8023 & PSL Research, Sorbonne Université, Université de Paris, Paris, France
Georges Debrégeas
bSorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin (LJP), Paris, France
Rémi Monasson
aLaboratory of Physics of the Ecole Normale Supérieure, CNRS UMR 8023 & PSL Research, Sorbonne Université, Université de Paris, Paris, France
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Posted February 04, 2022.
Emergence of time persistence in an interpretable data-driven neural network model
Sebastien Wolf, Guillaume Le Goc, Simona Cocco, Georges Debrégeas, Rémi Monasson
bioRxiv 2022.02.02.478841; doi: https://doi.org/10.1101/2022.02.02.478841
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