Journal
JOURNAL OF NEUROSCIENCE
Volume 36, Issue 38, Pages 9805-9816Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1067-16.2016
Keywords
adaptation; Hebbian learning; normalization; V1
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Funding
- National Institutes of Health/National Eye Institute [R01-EY019693, R01-EY08266]
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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.
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