Bio-inspired Model Based on Global-Local Hybrid Learning in Spiking Neural Network

Bringing machines up to human-level visual processing capabilities is an attractive research topic for decades. Deep neural networks (DNNs), inspired by the hierarchical structure of the human primary visual cortex at a macroscopic level, have achieved state-of-the-art performance in many applications. However, their practical applications remain limited due to the requisition of massive computing resources. Spiking neural networks (SNNs) simulate the spike-based information process of the biological neural system from the microscopic view and hold greater potential to ultra-low-power computations. In this paper, we imitate the human visual system from both the micro and macro scales and make the following contributions: (1) Inspired by the lateral effect between real neurons, we propose a Global-Local Hybrid Spike-Timing-Dependent Plasticity (GLHSTDP) algorithm that combines STDP with lateral synaptic learning mechanism, to train the spiking neural network. (2) We construct a deep spiking neural network (DSNN) to mimic the visual information processing mechanism in the human brain. Experimental results demonstrate that the proposed DSNN model equipped with the proposed learning algorithm works in a totally spike-based manner and achieve competitive accuracies on both the Caltech 101 and the MNIST datasets.