Neural network computing uses the structure of neurons in the brain as a model for parallel-processing computers. There are numerous models which differ in detail but share common features including: the training of networks; simple computational elements with large numbers of interconnections; interconnection strengths are real-valued analogue quantities; and the network forms internal representations capturing subtle correlations. The authors describe the recent advances, particularly in the UK.
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