Neuromorphic electronic systems | IEEE Journals & Magazine | IEEE Xplore
Scheduled Maintenance: On Monday, 30 June, IEEE Xplore will undergo scheduled maintenance from 1:00-2:00 PM ET (1800-1900 UTC).
On Tuesday, 1 July, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC).
During these times, there may be intermittent impact on performance. We apologize for any inconvenience.

Neuromorphic electronic systems


Abstract:

It is shown that for many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological s...Show More

Abstract:

It is shown that 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 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.<>
Published in: Proceedings of the IEEE ( Volume: 78, Issue: 10, October 1990)
Page(s): 1629 - 1636
Date of Publication: 06 August 2002

ISSN Information:


Contact IEEE to Subscribe

References

References is not available for this document.