Spiking Convolutional Deep Belief Networks

Understanding visual input as perceived by humans is a challenging task for machines. Today, most successful methods work by learning features from static images. Based on classical artificial neural networks, those methods are not adapted to process event streams as provided by the Dynamic Vision Sensor (DVS). Recently, an unsupervised learning rule to train Spiking Restricted Boltzmann Machines has been presented [9]. Relying on synaptic plasticity, it can learn features directly from event streams. In this paper, we extend this method by adding convolutions, lateral inhibitions and multiple layers. We evaluate our method on a self-recorded DVS dataset as well as the Poker-DVS dataset. Our results show that our convolutional method performs better and needs less parameters. It also achieves comparable results to previous event-based classification methods while learning features in an unsupervised fashion.