Synaptic plasticity and spike-based computation in VLSI networks of integrate-and-fire neurons
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Abstracteuromorphic 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 networks 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 spikebased 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 potential for exploiting future scaled VLSI processes and are ideal for implementing sensorymotor processing units on autonomous and humanoid robots.