Browsing Cognitive & Neural Systems by Author "Williamson, James R."

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Browsing Cognitive & Neural Systems by Author "Williamson, James R."

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  • Grossberg, Stephen; Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-05)
    A self-organizing architect is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-05)
    Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1995-02)
    A new neural network architecture for incremental supervised learning of analalog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an Adaptive ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1998-11)
    Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1994-10)
    A neural network is presented which explicity represents form attributes and relations between them, thus solving the binding problem without temporal coding. Rather, the network creates a graph representation by dynamically ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-10)
    This paper proposes a biologically-motivated neural network model of supervised learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal ...

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