Feature Competition in a Spike-Based Winner-Take-All VLSI Network

Abstract

Recurrent networks and hardware analogs that perform a winner-take-all computation have been studied extensively. This computation is rarely demonstrated in a spiking network of neurons receiving input spike trains. In this work, we demonstrate this computation not only within a VLSI network but also across networks of integrate-and-fire neurons in a feature competition task. The chip has four populations of neurons receiving input spike trains that represent the outputs of four feature maps. The connectivity within each population is configured so that all the neurons compete with one another. In addition, a second level of competition, which we call the feature competition, can be introduced between all populations (or feature maps). The two levels of competition are useful in a system that has to select both the locations of relevant features and the best feature map that is coded in an input stimulus. The selection process can be completed as fast as after two input spikes.