Speed estimation with propagation maps

We propose a neural architecture that estimates the speed of motion. The basis is a two-dimensional map made of locally connected integrate-and-fire neurons, that propagates and integrates synaptic input in a dendritic-cable-like manner, but irrespective of any direction. The propagation dynamics of such a map are tuned to filter preferred speeds: slow map dynamics filter slow speeds, fast map dynamics filter fast speeds. The propagation map is potentially simple enough for an analog hardware approach.