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A CONTINUOUS INPUT RAM-BASED STOCHASTIC NEURAL MODEL

GORSE, D; TAYLOR, JG; (1991) A CONTINUOUS INPUT RAM-BASED STOCHASTIC NEURAL MODEL. NEURAL NETWORKS , 4 (5) 657 - 665.

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Abstract

An extension of the probabilistic random access memory (pRAM) neural model is presented, which is shown to have a natural capacity for generalisation. This is displayed in one-and two-dimensional spatial learning tasks, using a form of reinforcement training. The model is then further extended to allow for the learning of temporal sequences, and this capacity is demonstrated in a simple temporal learning problem.

Type: Article
Title: A CONTINUOUS INPUT RAM-BASED STOCHASTIC NEURAL MODEL
Keywords: RAMS, PRAMS, STOCHASTIC MODELS, GENERALIZATION, REINFORCEMENT, SPATIOTEMPORAL LEARNING, NETWORKS, NETS
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/67325
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