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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
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. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 1021–1026). https://doi.org/10.1007/11550907_161
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