Synaptic learning in VLSI-based artificial nerve cells

Andrew J. Laffely, Seth Wolpert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we present a VLSI method for analog synaptic learning in an electronic neuronal model. This method reduces the size and complexity involved in implementing adaptive neuronally-based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulsedriven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitatory input over a wide range.

Original languageEnglish (US)
Title of host publication1993 IEEE 19th Annual Northeasrt Bioengineering Conference
PublisherPubl by IEEE
Pages103-105
Number of pages3
ISBN (Print)0780309251
StatePublished - 1993
EventProceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference - Newark, NJ, USA
Duration: Mar 18 1993Mar 19 1993

Other

OtherProceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference
CityNewark, NJ, USA
Period3/18/933/19/93

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)

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  • Cite this

    Laffely, A. J., & Wolpert, S. (1993). Synaptic learning in VLSI-based artificial nerve cells. In 1993 IEEE 19th Annual Northeasrt Bioengineering Conference (pp. 103-105). Publ by IEEE.