Analog circuits for relaxation networks

Selected examples are presented of recent advances, primarily from the U.S. and Canada, in analog circuits for relaxation networks. Relaxation networks having feedback connections exhibit potentially greater computational power per neuron than feedforward networks. They are also more poorly understood especially with respect to learning algorithms. Examples are described of analog circuits for (i) supervised learning in deterministic Boltzmann machines, (ii) unsupervised competitive learning and feature maps and (iii) networks with resistive grids for vision and audition tasks. We also discuss recent progress on in-circuit learning and synaptic weight storage mechanisms.