{
  "id": "2009.08889",
  "version": "v1",
  "published": "2020-09-18T15:28:28.000Z",
  "updated": "2020-09-18T15:28:28.000Z",
  "title": "Large Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction",
  "authors": [
    "Alexander van Meegen",
    "Tobias Kühn",
    "Moritz Helias"
  ],
  "comment": "9 pages, 3 figures",
  "categories": [
    "cond-mat.dis-nn",
    "q-bio.NC"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-09-18T15:28:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "random recurrent neuronal networks",
      "large deviation approach",
      "rate function",
      "activity prediction",
      "parameter inference"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}