Journal Article FZJ-2021-04016

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Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions

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2021
APS College Park, Md.

Physical review letters 127(15), 158302 () [10.1103/PhysRevLett.127.158302]

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Abstract: We here unify the field-theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence which enables data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Lastly, we expose a regime with fluctuation-induced transitions between mean-field solutions.

Classification:

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. 5231 - Neuroscientific Foundations (POF4-523) (POF4-523)
  2. MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) (HGF-SMHB-2014-2018)
  3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
  4. RenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A) (BMBF-01IS19077A)
  5. SDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005) (PF-JARA-SDS005)
  6. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2021
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 Record created 2021-10-27, last modified 2024-03-13