{
  "id": "1706.04265",
  "version": "v1",
  "published": "2017-06-13T21:57:53.000Z",
  "updated": "2017-06-13T21:57:53.000Z",
  "title": "Transfer entropy-based feedback improves performance in artificial neural networks",
  "authors": [
    "Sebastian Herzog",
    "Christian Tetzlaff",
    "Florentin Wörgötter"
  ],
  "categories": [
    "cs.LG",
    "cs.IT",
    "cs.NE",
    "math.IT"
  ],
  "abstract": "The structure of the majority of modern deep neural networks is characterized by uni- directional feed-forward connectivity across a very large number of layers. By contrast, the architecture of the cortex of vertebrates contains fewer hierarchical levels but many recurrent and feedback connections. Here we show that a small, few-layer artificial neural network that employs feedback will reach top level performance on a standard benchmark task, otherwise only obtained by large feed-forward structures. To achieve this we use feed-forward transfer entropy between neurons to structure feedback connectivity. Transfer entropy can here intuitively be understood as a measure for the relevance of certain pathways in the network, which are then amplified by feedback. Feedback may therefore be key for high network performance in small brain-like architectures.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-13T21:57:53.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "transfer entropy-based feedback",
      "performance",
      "few-layer artificial neural network",
      "vertebrates contains fewer hierarchical levels",
      "modern deep neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}