FPGA Implementation of Simplified Spiking Neural Network

Spiking Neural Networks (SNN) are third generation Artificial Neural Networks (ANN), which are close to the biological neural system. In recent years SNN has become popular in the area of robotics and embedded applications, therefore, it has become imperative to explore its real-time and energy-efficient implementations. SNNs are more powerful than their predecessors because of their ability to encode temporal information and to use biologically plausible plasticity rules. In this paper, a simpler and computationally efficient SNN model is described. The proposed model is implemented and validated utilizing a Xilinx Virtex 6 FPGA. It is demonstrated that the proposed model analyzes a fully connected network consisting of 800 neurons and 12,544 synapses in real-time.