A Spike-Based Saccadic Recognition System

Abstract

Abstract-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-firstspike 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.

A Spiking Neuron Improved PCA Model for Hand Gesture Recognition

“…Author defined the middle layer associated work under the recognition process so that effective object recognition over the dataset is presented. Matthias Oster [3] has defined spiking neuron based recognition process under the multi neuron classifier. Author defined a spiking information analysis approach under movement specification and definition.…”