A spiking neural network for extraction of features in colour opponent visual pathways and FPGA implementation

The human vision system is one of the most complex and the most superior systems that have been known to date. Inspired by the visual ventral pathways, a hierarchical spiking neural network is proposed to extract features from colour opponent visual pathways. The network is composed of multiple visual processing channels to simulate processing mechanism of the visual system. The firing rate map of each channel is recorded and further processed. Useful features are extracted from firing rate map with the characteristics of colour contrast sensitivity, orientation selective and illuminant constancy. These characteristics greatly enrich the expression of colour features and enhance the ability of object recognition. Simulation algorithm of the spiking neural network is based on integrated-and-fire neuron model and a set of receptive fields. Simulation results show that the network has the merit of capability of dealing with complex pattern recognition tasks. Furthermore, a SNN blockset is developed to implement the spiking neural network on FPGAs. It greatly speeds up the hardware modeling of the spiking neural network so that SNNs can be efficiently used in the hardware systems such as robots.