Invariant pattern recognition by means of fast synaptic plasticity

Joachim Buhmann, Klaus Schulten

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

Abstract

A two-layer neural system for shift-invariant pattern recognition is proposed. Model neurons are endowed with physiological dynamics involving membrane potentials and axonic spikes. Synapses between the two layers are plastic and change according to spike coincidences (Hebbian rules). The first neural network (encoder network) extracts features from a presented pattern and codes the neighborhood relationship of features by coincident activity of neurons. The second network (memory network) has stored several patterns. During recognition of a presented pattern the neural system establishes a strong projection between the first and the second layer, enhances activity in the set of those neurons represent the presented patterns, and supresses activity of other neurons. Synaptic plasticity according to Hebbian rules make it possible to generate a projection which preserves feature neighborhood relationships.

Original languageEnglish (US)
Title of host publicationIEEE Int Conf on Neural Networks
PublisherPubl by IEEE
Pages125-132
Number of pages8
StatePublished - 1988

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Invariant pattern recognition by means of fast synaptic plasticity'. Together they form a unique fingerprint.

  • Cite this

    Buhmann, J., & Schulten, K. (1988). Invariant pattern recognition by means of fast synaptic plasticity. In IEEE Int Conf on Neural Networks (pp. 125-132). Publ by IEEE.