Publication Summary and Abstract
Pearson M, Gilhespy I, Gurney K, Melhuish C, Mitchinson B, Nibouche M, Pipe A (2005), A real-time, FPGA based, biologically plausible neural network processor, proceedings of International Conference on Artificial Neural Networks (ICANN), Warsaw, Springer LNCS 3697:1021-1026.
A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented. This has been designed, as part of a collaborative project, to investigate and implement biologically plausible models of the rodent vibrissae based somatosensory system to control a robot. An emphasis has been made on hard real-time performance of the processor, as it is to be used as part of a feedback control system. This has led to a revision of some of the established modelling protocols used in other hardware spiking neural network processors. The underlying neuron model has the ability to model synaptic noise and inter-neural propagation delays to provide a greater degree of biological plausibility. The processor has been demonstrated modelling real neural circuitry in real-time, independent of the underlying neural network activity.
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