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ICANN
2005
Springer

A Real-Time, FPGA Based, Biologically Plausible Neural Network Processor

9 years 9 months ago
A Real-Time, FPGA Based, Biologically Plausible Neural Network Processor
Abstract. 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.
Martin J. Pearson, Ian Gilhespy, Kevin N. Gurney,
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICANN
Authors Martin J. Pearson, Ian Gilhespy, Kevin N. Gurney, Chris Melhuish, Benjamin Mitchinson, Mokhtar Nibouche, Anthony G. Pipe
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