Theory of diffusible messenger and learning in neural networks

Research output: Contribution to journalConference article

Abstract

Nitric oxide (NO) is a newly discovered neuronal messenger which transmits information in brain by way of diffusion. This phenomenon suggests a non-localized form of learning in computational neural network models. Based on a new dynamical description of single neuron learning, we demonstrate that NO diffusion can speed up the learning as well as reduce noise when a neuron is storing a pattern. Based on this idea we present the theory and application of a competitive learning algorithm that simulates pattern identification and classification in neural networks.

Original languageEnglish (US)
Pages (from-to)1383-1384
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 1997
EventProceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA
Duration: Oct 30 1997Nov 2 1997

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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