Spike-based learning in VLSI networks of integrate-and-fire neurons

Abstract

As the number of VLSI implementations of spike-based neural networks is steadily increasing, and the development of spike-based multi-chip systems is becoming more popular it is important to design spike-based learning algorithms and circuits, compatible with existing solutions, that endow these systems with adaptation and classification capabilities. We propose a spike-based learning algorithm that is highly effective in classifying complex patterns in semi-supervised fashion, and present neuromorphic circuits that support its VLSI implementation. We describe the architecture of a spike-based learning neural network, the analog circuits that implement the synaptic learning mechanism, and present results from a prototype VLSI chip comprising a full network of integrate-and-fire neurons and plastic synapses. We demonstrate how the VLSI circuits proposed reproduce the learning model's properties and fulfil its basic requirements for classifying complex patterns of mean firing rates.