Synaptic Plasticity and Spike-based Computation in VLSI Networks of Integrate-and-Fire Neurons

Abstract

Neuromorphic circuits are being used to develop a new generation of computing technologies based on the organizing principles of the biological nervous system. Within this context, we present neuromorphic circuits for implementing massively parallel VLSI net- works of integrate-and-fire neurons with adaptation and spike-based plasticity mechanisms. We describe both analog continuous time and digital asynchronous event-based circuits for constructing spiking neural network devices, and present a VLSI implementation of a spike- based learning mechanisms for carrying out robust classification of spatio-temporal patterns, and real-time sensory signal processing. We argue that these types of devices have great po- tential for exploiting future scaled VLSI processes and are ideal for implementing sensory- motor processing units on autonomous and humanoid robots.