A Spike-Based Saccadic Recognition System

We present a spike-based saccadic recognition system that uses a temporal-derivative silicon retina on a pan-tilt unit and an aVLSI multi-neuron classifier with a time-to-first-spike output coding. By using the spike information during the last 150 ms of a saccadic movement, we generate a reliable, sparse stimulus representation of image patches. We describe a novel classification scheme where the retinal spikes during this time influence the time-to-first spike of classifier neurons which receive the same constant input current. The preferred pattern of the neuron is stored in the synaptic connectivity between the retina and the classifier neuron. We demonstrate the robustness and real-time performance of this recognition scheme on a saccadic system which uses analog VLSI components.