{
  "id": "2309.14973",
  "version": "v1",
  "published": "2023-09-26T14:46:44.000Z",
  "updated": "2023-09-26T14:46:44.000Z",
  "title": "Linking Network and Neuron-level Correlations by Renormalized Field Theory",
  "authors": [
    "Michael Dick",
    "Alexander van Meegen",
    "Moritz Helias"
  ],
  "categories": [
    "cond-mat.dis-nn"
  ],
  "abstract": "It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well-suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analogue of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-09-26T14:46:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "renormalized field theory",
      "neuron-level correlations",
      "linking network",
      "multiple temporal scales",
      "single-unit autocorrelation function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}