{
  "id": "0704.1007",
  "version": "v2",
  "published": "2007-04-08T02:10:58.000Z",
  "updated": "2007-12-07T07:14:19.000Z",
  "title": "Transient Dynamics of Sparsely Connected Hopfield Neural Networks with Arbitrary Degree Distributions",
  "authors": [
    "Pan Zhang",
    "Yong Chen"
  ],
  "comment": "11 pages, 5 figures. Any comments are favored",
  "journal": "Physica A 387, 1009(2008)",
  "doi": "10.1016/j.physa.2007.09.047",
  "categories": [
    "cond-mat.dis-nn",
    "cond-mat.stat-mech"
  ],
  "abstract": "Using probabilistic approach, the transient dynamics of sparsely connected Hopfield neural networks is studied for arbitrary degree distributions. A recursive scheme is developed to determine the time evolution of overlap parameters. As illustrative examples, the explicit calculations of dynamics for networks with binomial, power-law, and uniform degree distribution are performed. The results are good agreement with the extensive numerical simulations. It indicates that with the same average degree, there is a gradual improvement of network performance with increasing sharpness of its degree distribution, and the most efficient degree distribution for global storage of patterns is the delta function.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2007-12-07T07:14:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "sparsely connected hopfield neural networks",
      "arbitrary degree distributions",
      "transient dynamics",
      "uniform degree distribution"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "journal": "Physica A Statistical Mechanics and its Applications",
      "year": 2008,
      "month": "Feb",
      "volume": 387,
      "number": 4,
      "pages": 1009
    },
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2008PhyA..387.1009Z"
    }
  }
}