A bio-inspired feedforward system for categorization of AER motion events

This paper introduces an event based feedforward categorization system, which takes data from a temporal contrast Address Event Presentation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using AER based tempotron classifier (a network of leaky integrate-and-fire (LIF) spiking neurons). One appealing character of our system is the event-driven processing. The input and the features are both in the form of address events (spikes). Experimental results on a posture dataset have proved the efficacy of the proposed system.