Emergence of filters from natural scenes in a sparse spike coding scheme

As an alternative to classical representations in machine learning algorithms, we explore coding strategies using events as is observed for spiking neurons in the central nervous system. Focusing on visual processing, we have previously shown that we may define a sparse spike coding scheme by implementing accordingly lateral interactions [14]. This class of algorithms is both compatible with biological constraints and also to neurophysiological observations and yields a performant algorithm of computing by events. We explore here learning mechanisms to unsupervisely derive an optimal overcomplete set of filters based on previous work of Olshausen and Field [10] and show its biological relevance.