This chapter reviews advances in hardware learning and adaptation in synthetic neural
systems. Research in the field draws inspiration from neurobiology, physics of computation …

Learning on Silicon combines models of adaptive information processing in the brain with
advances in microelectronics technology and circuit design. The premise is to construct …

Neural networks are capable of solving complex tasks (eg speech and handwriting
recognition) based on the presentation of a large number of examples. During learning …

It is striking that after all advances in microelectronic technology, computers are still too
dumb to perform any task autonomously, without instruction by human beings or implicit …

A computation is an operation that can be performed by a physical machine. We are familiar
with digital computers: Machines based on a simple logic function (the binary NOR) and …

The present thesis is a partial fulfillment of the requirements for the degree of Doctor of
Philosophy (licentiatgraden, Philosophiae Doctor, Ph. D.). The work was carried out at the …

Neural models are used in both computational neuroscience and in pattern recognition. The
aim of the first is understanding of real neural systems, and of the second is gaining better …

Neural models are used in both computational neuroscience and in pattern recognition. The
aim of the first is understanding of real neural systems, and of the second is gaining better …

This paper explains the various models of learning in artificial neural networks which are
appropriate for implementation as analog VLSI circuits and systems. We do not cover the …

This paper provides a tutorial of various VLSI approaches to synthesizing artificial neural
networks as microelectronic systems. The means by which the network learns and the …