{
  "id": "cond-mat/9705182",
  "version": "v1",
  "published": "1997-05-19T11:16:44.000Z",
  "updated": "1997-05-19T11:16:44.000Z",
  "title": "Stochastic learning in a neural network with adapting synapses",
  "authors": [
    "G. Lattanzi",
    "G. Nardulli",
    "G. Pasquariello",
    "S. Stramaglia"
  ],
  "comment": "25 pages, LaTeX file",
  "doi": "10.1103/PhysRevE.56.4567",
  "categories": [
    "cond-mat.dis-nn",
    "q-bio"
  ],
  "abstract": "We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\\to\\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.",
  "revisions": [
    {
      "version": "v1",
      "updated": "1997-05-19T11:16:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural network",
      "adapting synapses",
      "input neurons chosen",
      "parallel stochastic dynamics",
      "states variables"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. E"
    },
    "note": {
      "typesetting": "LaTeX",
      "pages": 25,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}