FPGA Based Silicon Spiking Neural Array

Abstract

Rapid design time, low cost, flexibility, digital precision, and stability are characteristics that favor FPGAs as a promising alternative to analog VLSI based approaches for designing neuromorphic systems. High computational power as well as low size, weight, and power (SWAP) are advantages that FPGAs demonstrate over software based neuromorphic systems. We present an FPGA based array of Leaky-Integrate and Fire (LIF) artificial neurons. Using this array, we demonstrate three neural computational experiments: auditory Spatio-Temporal Receptive Fields (STRFs), a neural parameter optimizing algorithm, and an implementation of the Spike Time Dependant Plasticity (STDP) learning rule.