Autonomous Perceptual Feature Extraction in a Topology-Constrained Architecture
نویسنده
چکیده
In this paper, it is shown that the Feature-Extracting Bidirectional Associative Memory (FEBAM) can encompass competitive model features based on winner-take-all, kwinners-take-all and self-organizing feature map properties. The modified model achieves perceptual multidimensional feature extraction, cluster-based category formation through simultaneous creation of prototype/exemplar memories, and topological dimensionality reduction. FEBAM is shown to evolve from locally-coded (prototype-based) to distributed or “sparsely-coded” (exemplar-based) representations.
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