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000885635 0247_ $$2arXiv$$aarXiv:2009.08889
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000885635 1001_ $$0P:(DE-Juel1)173607$$avan Meegen, Alexander$$b0$$eCorresponding author$$ufzj
000885635 245__ $$aLarge Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction
000885635 260__ $$c2020
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000885635 520__ $$aStatistical 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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000885635 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
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000885635 588__ $$aDataset connected to arXivarXiv
000885635 7001_ $$0P:(DE-Juel1)164473$$aKühn, Tobias$$b1
000885635 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
000885635 8564_ $$uhttps://arxiv.org/abs/2009.08889
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000885635 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x1
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