Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

摘要

Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions forfast simulations of spiking neural networks. While these architectures are useful for exploring the computationalproperties of large-scale models of the nervous system, the challenge of building low-power compact physical artifactsthat can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, wepropose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphiccircuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistictemporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-basedplasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them;we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuitscan implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed withexperimental results obtained from our own circuits and systems, and argue how the circuits and networks presented inthis work represent a useful set of components for efficiently and elegantly implementing neuromorphiccognition.