Neuromorphic circuit based on the un-supervised learning of biologically inspired spiking neural network for pattern recognition

Abstract

One of the most sophisticated platforms for hosting intelligent systems is bio-inspired. This study proposes pattern recognition hardware using a biologically inspired Spiking Neural Network (SNN) and the new dimensionality reduction approach. The SNN model is based on real neural networks consisting of spiking neurons linked by excitatory and inhibitory synapses activated by excitatory and inhibitory neurotransmitters. Also, a semi-supervised (un-supervised STDP based learning with supervised weight initialization), spike-based learning strategy based on the learning procedure of the nervous system is used to teach the spiking output layer neurons, albeit that the hardware implementation benefits from a semi-supervised approach. The goal of this research is to accurately categorize patterns in the MNIST and CIFAR10 datasets using an SNN-based hardware platform. Due to the limitations of the latter’s resources, a dimensionality reduction based on principal component analysis (PCA) is proposed to speed up the processing procedure and reduce the hardware implementation cost. The presented pattern recognition platform is implemented using the Xilinx® VIVADO high-level synthesis platform (HLS). Finally, optimization approaches are used to improve the used space, reduce hardware implementation delay, and speed up the design process.