Pattern adaptation and normalization reweighting

Zachary M. Westrick, David J. Heeger, Michael S. Landy

Research output: Contribution to journalArticlepeer-review

Abstract

Adaptation to an oriented stimulus changes both the gain and preferred orientation of neural responses in V1. Neurons tuned near the adapted orientation are suppressed, and their preferred orientations shift away from the adapter. We propose a model in which weights of divisive normalization are dynamically adjusted to homeostatically maintain response products between pairs of neurons. We demonstrate that this adjustment can be performed by a very simple learning rule. Simulations of this model closely match existing data from visual adaptation experiments. We consider several alternative models, including variants based on homeostatic maintenance of response correlations or covariance, as well as feedforward gain-control models with multiple layers, and we demonstrate that homeostatic maintenance of response products provides the best account of the physiological data.

Original languageEnglish (US)
Pages (from-to)9805-9816
Number of pages12
JournalJournal of Neuroscience
Volume36
Issue number38
DOIs
StatePublished - Sep 21 2016

Keywords

  • Adaptation
  • Hebbian learning
  • Normalization
  • V1

ASJC Scopus subject areas

  • Neuroscience(all)

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