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.

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Martin J. Pearson, Ian Gilhespy, Kevin N. Gurney, Chris Melhuish, Benjamin Mitchinson, Mokhtar Nibouche, Anthony G. Pipe
• Keywords: ICANN 2005, Leaky-integrate-and-fire Neural Network, Neural Network Processors, Spiking Neural Network