Pattern classification using teurons

M. Sipper, Y. Yeshurun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Neural networks consist of simple elements capable of summation and thresholding. The authors define a more general element, the task-oriented neuron or teuron, which can compute higher-order functions. They further define teuron networks and show two such networks that can be used as content addressable memories and as pattern classifiers. The first network is based on the Godel encoding scheme. It uses this scheme in order to memorize sequences of numbers. These sequences are then stored analogically. The second network uses binary encoding, i.e., a binary sequence is translated into its decimal equivalent and then stored analogically. The authors demonstrate the feasibility of implementing such networks. It is concluded that they can be implemented in such a way as to reduce cost, due to a reduction in the number of elements coupled with constancy of link values (synaptic weights).

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherPubl by IEEE
Number of pages5
ISBN (Print)0818620625
StatePublished - 1 Dec 1990
Externally publishedYes
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: 16 Jun 199021 Jun 1990

Publication series

NameProceedings - International Conference on Pattern Recognition


ConferenceProceedings of the 10th International Conference on Pattern Recognition
CityAtlantic City, NJ, USA

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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