Literature DB >> 18255036

Learning to link visual contours.

Wu Li1, Valentin Piëch, Charles D Gilbert.   

Abstract

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.

Mesh:

Year:  2008        PMID: 18255036      PMCID: PMC2409109          DOI: 10.1016/j.neuron.2007.12.011

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  43 in total

1.  Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency.

Authors:  Tai Sing Lee; Cindy F Yang; Richard D Romero; David Mumford
Journal:  Nat Neurosci       Date:  2002-06       Impact factor: 24.884

2.  Global contour saliency and local colinear interactions.

Authors:  Wu Li; Charles D Gilbert
Journal:  J Neurophysiol       Date:  2002-11       Impact factor: 2.714

3.  Contour integration in striate cortex. Classic cell responses or cooperative selection?

Authors:  Roman Bauer; Sabine Heinze
Journal:  Exp Brain Res       Date:  2002-10-01       Impact factor: 1.972

4.  Task-specific perceptual learning on speed and direction discrimination.

Authors:  Tiffany Saffell; Nestor Matthews
Journal:  Vision Res       Date:  2003-06       Impact factor: 1.886

5.  Lateral connectivity and contextual interactions in macaque primary visual cortex.

Authors:  Dan D Stettler; Aniruddha Das; Jean Bennett; Charles D Gilbert
Journal:  Neuron       Date:  2002-11-14       Impact factor: 17.173

6.  Perceptual learning and top-down influences in primary visual cortex.

Authors:  Wu Li; Valentin Piëch; Charles D Gilbert
Journal:  Nat Neurosci       Date:  2004-05-23       Impact factor: 24.884

7.  Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex.

Authors:  C D Gilbert; T N Wiesel
Journal:  Nature       Date:  1979-07-12       Impact factor: 49.962

8.  Clustered intrinsic connections in cat visual cortex.

Authors:  C D Gilbert; T N Wiesel
Journal:  J Neurosci       Date:  1983-05       Impact factor: 6.167

9.  Anatomical binding of intrinsic connections in striate cortex of tree shrews (Tupaia glis).

Authors:  K S Rockland; J S Lund; A L Humphrey
Journal:  J Comp Neurol       Date:  1982-07-20       Impact factor: 3.215

10.  The statistical reliability of signals in single neurons in cat and monkey visual cortex.

Authors:  D J Tolhurst; J A Movshon; A F Dean
Journal:  Vision Res       Date:  1983       Impact factor: 1.886

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  92 in total

1.  Learning-dependent plasticity with and without training in the human brain.

Authors:  Jiaxiang Zhang; Zoe Kourtzi
Journal:  Proc Natl Acad Sci U S A       Date:  2010-07-13       Impact factor: 11.205

2.  Perceptual learning beyond retinotopic reference frame.

Authors:  En Zhang; Wu Li
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-23       Impact factor: 11.205

3.  Adapting to altered image statistics using processed video.

Authors:  Michael Falconbridge; David Wozny; Ladan Shams; Stephen A Engel
Journal:  Vision Res       Date:  2009-04-11       Impact factor: 1.886

4.  Equivalent representation of real and illusory contours in macaque V4.

Authors:  Yanxia Pan; Minggui Chen; Jiapeng Yin; Xu An; Xian Zhang; Yiliang Lu; Hongliang Gong; Wu Li; Wei Wang
Journal:  J Neurosci       Date:  2012-05-16       Impact factor: 6.167

5.  Network model of top-down influences on local gain and contextual interactions in visual cortex.

Authors:  Valentin Piëch; Wu Li; George N Reeke; Charles D Gilbert
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-07       Impact factor: 11.205

6.  Heritability of human visual contour integration-an integrated genomic study.

Authors:  Zijian Zhu; Biqing Chen; Ren Na; Wan Fang; Wenxia Zhang; Qin Zhou; Shanbi Zhou; Han Lei; Ailong Huang; Tingmei Chen; Dongsheng Ni; Yuping Gu; Jianing Liu; Yi Rao; Fang Fang
Journal:  Eur J Hum Genet       Date:  2019-07-30       Impact factor: 4.246

Review 7.  Perceptual learning and adult cortical plasticity.

Authors:  Charles D Gilbert; Wu Li; Valentin Piech
Journal:  J Physiol       Date:  2009-06-15       Impact factor: 5.182

8.  Flexible learning of natural statistics in the human brain.

Authors:  D Samuel Schwarzkopf; Jiaxiang Zhang; Zoe Kourtzi
Journal:  J Neurophysiol       Date:  2009-07-15       Impact factor: 2.714

9.  Plasticity between neuronal pairs in layer 4 of visual cortex varies with synapse state.

Authors:  Ignacio Sáez; Michael J Friedlander
Journal:  J Neurosci       Date:  2009-12-02       Impact factor: 6.167

10.  Adult visual cortical plasticity.

Authors:  Charles D Gilbert; Wu Li
Journal:  Neuron       Date:  2012-07-26       Impact factor: 17.173

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