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Neuromorphic electronic systems

Mead, Carver (1990) Neuromorphic electronic systems. Proceedings of the IEEE, 78 (10). pp. 1629-1636. ISSN 0018-9219. https://resolver.caltech.edu/CaltechAUTHORS:20141222-113217405

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Abstract

Biological in formation-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer-scale silicon fabrication.


Item Type:Article
Related URLs:
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http://dx.doi.org/10.1109/5.58356DOIArticle
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=58356PublisherArticle
Additional Information:© Copyright 1990 IEEE. Manuscript received February 1, 1990; revised March 23, 1990. IEEE Log Number 9039181.
Issue or Number:10
Record Number:CaltechAUTHORS:20141222-113217405
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20141222-113217405
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:53090
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:22 Dec 2014 19:42
Last Modified:03 Oct 2019 07:47

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