{
  "id": "cond-mat/9610160",
  "version": "v1",
  "published": "1996-10-22T10:04:37.000Z",
  "updated": "1996-10-22T10:04:37.000Z",
  "title": "Storage capacity of correlated perceptrons",
  "authors": [
    "D. Malzahn",
    "A. Engel",
    "I. Kanter"
  ],
  "comment": "16 pages, Latex2e",
  "doi": "10.1103/PhysRevE.55.7369",
  "categories": [
    "cond-mat.dis-nn"
  ],
  "abstract": "We consider an ensemble of $K$ single-layer perceptrons exposed to random inputs and investigate the conditions under which the couplings of these perceptrons can be chosen such that prescribed correlations between the outputs occur. A general formalism is introduced using a multi-perceptron costfunction that allows to determine the maximal number of random inputs as a function of the desired values of the correlations. Replica-symmetric results for $K=2$ and $K=3$ are compared with properties of two-layer networks of tree-structure and fixed Boolean function between hidden units and output. The results show which correlations in the hidden layer of multi-layer neural networks are crucial for the value of the storage capacity.",
  "revisions": [
    {
      "version": "v1",
      "updated": "1996-10-22T10:04:37.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "storage capacity",
      "correlated perceptrons",
      "random inputs",
      "multi-layer neural networks",
      "correlations"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. E"
    },
    "note": {
      "typesetting": "LaTeX",
      "pages": 16,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}