Computational advantages of higher order neural networks

C. L. Giles, R. D. Griffin, T. Maxwell

Research output: Contribution to journalConference article

Abstract

This paper discusses recent research in higher order neural networks (HONNs) with a particular emphasis on geometric invariances. Motivation for the HONN model is that it is a natural extension of first order neural nets (power series expansion) and offers increased computational power for an artificial neuron. Though there are many ways to increase the computational power of the neuron, the HONN representation is a straight-forward one.

Original languageEnglish (US)
Number of pages1
JournalNeural Networks
Volume1
Issue number1 SUPPL
DOIs
StatePublished - Jan 1 1988
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: Sep 6 1988Sep 10 1988

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Neural networks
Neurons
Neural Networks (Computer)
Invariance
Research

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Giles, C. L. ; Griffin, R. D. ; Maxwell, T. / Computational advantages of higher order neural networks. In: Neural Networks. 1988 ; Vol. 1, No. 1 SUPPL.
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Computational advantages of higher order neural networks. / Giles, C. L.; Griffin, R. D.; Maxwell, T.

In: Neural Networks, Vol. 1, No. 1 SUPPL, 01.01.1988.

Research output: Contribution to journalConference article

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