Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity

This paper focuses on feedforward spiking neuron models of the visual cortex. Essentially, we show that a combination of a temporal coding scheme where the most strongly activated neurons fire first with Spike Timing-Dependent Plasticity leads to a situation where neurons will gradually become selective to visual patterns that are both salient, and consistently present in the inputs. At the same time, their responses become more and more rapid. These responses can then be used very effectively to perform object recognition in natural images. We firmly believe that such mechanisms are a key to understanding the remarkable efficiency of the primate visual system, and that similar mechanisms could and should be implemented in artificial vision systems, possibly using Address Event Representation (AER) and memristors. Subsequent work will explore video processing, the use of feedback connections, and oscillatory regimes.