A comparison of a hardware and a software integrate and fire neural network for clustering onsets in cochlear filtered sound

Onset clustering (which we use as part of a system for sound segmentation) uses integrate-and-fire neurons to perform across spectrum and across time clustering of increases in sound intensity in different parts of the spectrum. We show that a network of recently developed analogue VLSI integrate-and-fire neurons can perform this task in real-time, and compare its performance with a simulated network.