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 andThorpe 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 o2ers an alternative to the classical spike frequency coding scheme. We extended this model to e4cient 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 de6ne a visual spike code. In particular, this transform couldbe usedwith large andarbitrary d so that we may de6ne an over-complete representation which may de6ne an e4cient sparse spike coding scheme in arbitrary multi-layeredarchitectures. We show here extensions of this methodof computing with spike events, introducing an adaptive scheme leading to the emergence of V1-like receptive 6elds and then a model of bottom-up saliency pursuit. c
['Laurent Perrinet', 'Manuel Samuelides', 'Simon J. Thorpe']
Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit