Titlebook: VLSI for Neural Networks and Artificial Intelligence; José G. Delgado-Frias,William R. Moore Book 1994 Springer Science+Business Media New

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Analog VLSI Neural Learning Circuits — A Tutorialsystems. We do not cover the wider topic of analog VLSI neural networks in general, but restrict the presentation to circuits which perform in situ learning. Both supervised and unsupervised learning models are included.

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An Analog CMOS Implementation of a Kohonen Network with Learning Capabilityave been successfully used to solve various problems in fields like image processing or robot control. Unfortunately, although the task each cell has to accomplish is rather simple, computing time of software simulations becomes prohibitive as the size of a network increases. For real-time applicati

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Back-Propagation Learning Algorithms for Analog VLSI Implementationdvocates of digital VLSI NNs (D VNNs) (Ramacher, 1991) claim the higher computational accuracy, noise immunity and speed of digital implementations. On the other hand, analog VLSI NNs (AVNNs) are more closely related to the biological models and are better suited to exploit innovative circuit soluti

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An Analog Implementation of the Boltzmann Machine with Programmable Learning Algorithmsinsufficient, few analog integrated circuits have been realized for these algorithms, because neither their implementation nor their parallelization are obvious. On the other hand, Boltzmann Machines (Hinton et Sejnowski 1984) show a number of very attractive features, including high recognition rat

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