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005     20240313103121.0
024 7 _ |a arXiv:2009.08889
|2 arXiv
024 7 _ |a 2128/25884
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024 7 _ |a altmetric:90800774
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037 _ _ |a FZJ-2020-03976
100 1 _ |a van Meegen, Alexander
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245 _ _ |a Large Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction
260 _ _ |c 2020
336 7 _ |a Preprint
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336 7 _ |a WORKING_PAPER
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336 7 _ |a Electronic Article
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336 7 _ |a preprint
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336 7 _ |a ARTICLE
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520 _ _ |a Statistical field theory captures collective non-equilibrium dynamics of neuronal networks, but it does not address the inverse problem of searching the connectivity to implement a desired dynamics. We here show for an analytically solvable network model that the effective action in statistical field theory is identical to the rate function in large deviation theory; using field theoretical methods we derive this rate function. It takes the form of a Kullback-Leibler divergence and enables data-driven inference of model parameters and Bayesian prediction of time series.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
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536 _ _ |a 571 - Connectivity and Activity (POF3-571)
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
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536 _ _ |a MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
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|a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
588 _ _ |a Dataset connected to arXivarXiv
700 1 _ |a Kühn, Tobias
|0 P:(DE-Juel1)164473
|b 1
700 1 _ |a Helias, Moritz
|0 P:(DE-Juel1)144806
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856 4 _ |u https://arxiv.org/abs/2009.08889
856 4 _ |u https://juser.fz-juelich.de/record/885635/files/2009.08889.pdf
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856 4 _ |u https://juser.fz-juelich.de/record/885635/files/2009.08889.pdf?subformat=pdfa
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913 1 _ |a DE-HGF
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914 1 _ |y 2020
915 _ _ |a OpenAccess
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