Visual Strategies for Sparse Spike Coding

We explore visual spike coding strategies in a neural layer in order to build a dynamical model of primary vision. A strictly feed-forward architecture is compared to a strategy accounting for lateral interactions that shows sparse spike coding of the image as is observed in the primary visual areas [1]. This transform is defined over a neural layer according to a greedy matching pursuit scheme and we show that for natural images its coefficients may be simply carried by the rank of spike arrival. This model is easily expandable to multiple layers and despite its simplicity has applications to image compression comparable to industrial standards but also novel strategies for pattern detection.