Abstract
A model of neural processing is described which is able to incorporate a great deal of neurophysiological detail (synaptic noise, synapse-synapse interactions, cell surface geometry, temporal features) and which is capable of hardware realisation as a probabilistic random access memory’ (pRAM). The model can operate either in a binary mode or can integrate the effects of incoming spike trains to perform a real-to-binary mapping (integrating pRAM’ or i-pRAM). pRAMs can be trained using a variety of techniques, in particular reinforcement training, which has the advantage of being wholly realisable in hardware as well as having greater biological plausibility than supervised techniques. © 1992, Walter de Gruyter. All rights reserved.
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