Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit

Abstract

In order to account for the rapidity of visual processing, we explore visual coding strategies using a one-pass feed-forward spiking neural network. We based our model on the work of Van Rullen and Thorpe Neural Comput. 13 (6) (2001) 1255, which constructs a retinal representation using an orthogonal wavelet transform. This strategy provides a spike code, thanks to a rank order coding scheme which offers an alternative to the classical spike frequency coding scheme. We extended this model to efficient representations in arbitrary linear generative models by implementing lateral interactions on top of this feed-forward model. This method uses a matching pursuit scheme—recursively detecting in the image the best match with the elements of a dictionary and then subtracting it—and which may similarly define a visual spike code. In particular, this transform could be used with large and arbitrary dictionaries, so that we may define an over-complete representation which may define an efficient sparse spike coding scheme in arbitrary multi-layered architectures. We show here extensions of this method of computing with spike events, introducing an adaptive scheme leading to the emergence of V1-like receptive fields and then a model of bottom-up saliency pursuit.