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1 July 1992 Spatiotemporal neurons and local learning rules enabling massively parallel neurocomputers
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In this paper we present a way to provide neural networks, at the same time, both with a natural notion of time and with locality and modularity in computation. By doing so we ease massively parallel implementations. At the neuron level we introduce pulse code cable neurons, a neuron model with spatio-temporal information processing capabilities and much reduced communication bandwidth; its constituting parts either are branched, one-dimensional electrically equivalent cables of neuronal membrane in which all information processing takes place locally, or 1 bit delayed interconnections that unidirectionally connect one membrane to another. At the network level we argue that the theory of Neuronal Group Selection is an apt candidate for providing modularity by means of its `group-forming' local learning rules. We show that, taking dimensions from biological reality, the overall computation time scales with the spatial and temporal accuracy with which we model a membrane, rather than with the number of neurons or synapses. Routing the interconnections remains a problem, but with current technology real-time simulation of some millions of interconnections seems readily feasible.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arno J. Klaassen "Spatiotemporal neurons and local learning rules enabling massively parallel neurocomputers", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140140;

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