Preprint

FZJ-2020-03976

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Large Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction

van Meegen, A. (Corresponding author)FZJ* ; Kühn, T. ; Helias, M.FZJ*

2020

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Please use a persistent id in citations: http://hdl.handle.net/2128/25884

Abstract: 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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
2. 571 - Connectivity and Activity (POF3-571) (POF3-571)
3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
4. MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) (HGF-SMHB-2014-2018)
5. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2020

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