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A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as filled-in brightnesses. Top-down feedback interactions are needed in addition to bottom-up feed-forward interactions to simulate these data. One feedback loop is modeled between lateral(More)
Despite the fact that many symbolic and connec-tionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little Is known regarding their comparative strengths and weaknesses. This paper presents the results of experiments comparing the ID3 symbolic learning algorithm with the perceptron and(More)
We present a computer algorithm for the automated assignment of polypeptide backbone and 13C beta resonances of a protein of known primary sequence. Input to the algorithm consists of cross peaks from several 3D NMR experiments: HNCA, HN(CA)CO, HN(CA)HA, HNCACB, COCAH, HCA(CO)N, HNCO, HN(CO)CA, HN(COCA)HA, and CBCA(CO)NH. Data from these experiments(More)
  • Alan Gove, Stephen Grossbergy, Ennio Mingollaz
  • 1995
A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as lled-in brightnesses. Top-down feedback interactions are needed in addition to bottom-up feedforward interactions to simulate these data. One feedback loop is modeled between lateral(More)
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