{
  "id": "cond-mat/0511159",
  "version": "v2",
  "published": "2005-11-07T13:48:01.000Z",
  "updated": "2005-12-09T14:17:08.000Z",
  "title": "Learning by message-passing in networks of discrete synapses",
  "authors": [
    "Alfredo Braunstein",
    "Riccardo Zecchina"
  ],
  "comment": "4 pages, 3 figures; references updated and minor corrections; accepted in PRL",
  "journal": "Phys. Rev. Lett. 96, 030201 (2006)",
  "doi": "10.1103/PhysRevLett.96.030201",
  "categories": [
    "cond-mat.dis-nn",
    "cs.LG",
    "q-bio.NC"
  ],
  "abstract": "We show that a message-passing process allows to store in binary \"material\" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g. $n\\simeq10^{5}-10^{6}$). The algorithm can be turned into an on-line --fault tolerant-- learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2005-12-09T14:17:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "discrete synapses",
      "information theoretic bounds",
      "random patterns",
      "wide range",
      "connection topologies"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. Lett."
    },
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}