Spike-Based Convolutional Network for real-time processing

Abstract

In this paper we propose the first bio-inspired sixlayer convolutional network (ConvNet) non-frame based that can be implemented with already physically available spikebased electronic devices. The system was designed to recognize people in three different positions: standing, lying or up-sidedown. The inputs were spikes obtained with a motion retina chip. We provide simulation results showing recognition delays of 16 milliseconds from stimulus onset (time-to-first spike) with a recognition rate of 94%. The weight sharing property in ConvNets and the use of AER protocol allow a great reduction in the number of both trainable parameters and connections (only 748 trainable parameters and 123 connections in our AER system (out of 506998 connections that would be required in a frame-based implementation).