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Hardware implementations of spiking neuron models have been studied over the years mainly in researches focused on bio-inspired systems and computational neuroscience. This introduced considerable challenges for researchers particularly in terms of the requirements to realise a efficient embedded solution which may provide artificial devices adaptability and performance in real-time environment. Thus, programmable hardware was widely used as a model for the adaptable requirements of neural networks. From this perspective, this paper describes an efficient implementation of a realistic spiking neuron model on a Field Programmable Gate Array (FPGA). A network consisting of 10 Izhikevich’s neurons was produced, in a low-cost and low-density FPGA. It operates 100 times faster than in real time, and the perspectives of these results in newer models of FPGAs are promising.

AN EFFICIENT IMPLEMENTATION OF A REALISTIC SPIKING NEURON MODEL ON AN FPGA