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Top-Down Inputs Enhance Orientation Selectivity in Neurons of the Primary Visual Cortex during Perceptual Learning

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Top-Down Inputs Enhance Orientation Selectivity in Neurons of the Primary Visual Cortex during Perceptual Learning
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003770
Pubmed ID
Authors

Samat Moldakarimov, Maxim Bazhenov, Terrence J. Sejnowski

Abstract

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.

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The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Switzerland 2 2%
United Kingdom 2 2%
Netherlands 1 <1%
Germany 1 <1%
France 1 <1%
China 1 <1%
Belarus 1 <1%
Unknown 104 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 32%
Researcher 24 21%
Student > Master 12 10%
Student > Bachelor 7 6%
Professor > Associate Professor 6 5%
Other 16 14%
Unknown 14 12%
Readers by discipline Count As %
Neuroscience 35 30%
Agricultural and Biological Sciences 22 19%
Psychology 18 15%
Computer Science 11 9%
Physics and Astronomy 2 2%
Other 9 8%
Unknown 20 17%