title: Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.
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.
authors.affiliation: Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America; The Institute for Neural Computation, University of California, San Diego, La Jolla, California, United States of America.
authors.forename: Samat
authors.initials: S
authors.lastname: Moldakarimov
authors.affiliation: Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America; Department of Cell Biology and Neuroscience, University of California, Riverside, Riverside, California, United States of America.
authors.forename: Maxim
authors.initials: M
authors.lastname: Bazhenov
authors.affiliation: Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America; The Institute for Neural Computation, University of California, San Diego, La Jolla, California, United States of America; Division of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America.
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D019295:Computational Biology; D056228:Feedback, Sensory / Q000502:physiology; D000882:Haplorhini; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*