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Spiking Neural Networks (SNN) are third-generation Artificial Neural Networks(ANN) which are close to the biological neural system. In recent years SNN hasbecome popular in the area of robotics and embedded applications, therefore, ithas become imperative to explore its real-time and energy-efficientimplementations. SNNs are more powerful than their predecessors because theyencode temporal information and use biologically plausible plasticity rules. Inthis paper, a simpler and computationally efficient SNN model using FPGAarchitecture is described. The proposed model is validated on a Xilinx Virtex 6FPGA and analyzes a fully connected network which consists of 800 neurons and12,544 synapses in real-time.

FPGA Implementation of Simplified Spiking Neural Network