Global processing of visual stimuli in a neural network of coupled oscillators

H. Sompolinsky, D. Golomb, D. Kleinfeld

Research output: Contribution to journalArticlepeer-review

140 Scopus citations

Abstract

An oscillator neural network model is presented that is capable of processing local and global attributes of sensory input. Local features in the input are encoded in the average firing rate of the neurons while the relationships between these features can modulate the temporal structure of the neuronal output. Neurons that share the same receptive field interact via relatively strong feedback connections, while neurons with different fields interact via specific, relatively weak connections. This pattern of connectivity mimics that of primary visual cortex. The model is studied in the context of processing visual stimuli that are coded for orientation. We compare our theoretical results with recent experimental evidence on coherent oscillatory activity in the cat visual cortex. The computational capabilities of the model for performing discrimination and segmentation tasks are demonstrated.

Original languageEnglish
Pages (from-to)7200-7204
Number of pages5
JournalProceedings of the National Academy of Sciences of the United States of America
Volume87
Issue number18
DOIs
StatePublished - 1 Jan 1990
Externally publishedYes

Keywords

  • orientation tuning
  • parallel computation
  • primary visual cortex
  • statistical mechanics
  • temporal coherence

ASJC Scopus subject areas

  • General

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