Literature DB >> 16987926

Spectral receptive field properties explain shape selectivity in area V4.

Stephen V David1, Benjamin Y Hayden, Jack L Gallant.   

Abstract

Neurons in cortical area V4 respond selectively to complex visual patterns such as curved contours and non-Cartesian gratings. Most previous experiments in V4 have measured responses to small, idiosyncratic stimulus sets and no single functional model yet accounts for all of the disparate results. We propose that one model, the spectral receptive field (SRF), can explain many observations of selectivity in V4. The SRF describes tuning in terms of the orientation and spatial frequency spectrum and can, in principle, predict the response to any visual stimulus. We estimated SRFs for neurons in V4 of awake primates by linearized reverse correlation of responses to a large set of natural images. We find that V4 neurons have large orientation and spatial frequency bandwidth and often bimodal orientation tuning. For comparison, we estimated SRFs for neurons in primary visual cortex (V1). Consistent with previous observations, we find that V1 neurons have narrower bandwidth than that of V4. To determine whether estimated SRFs can account for previous observations of selectivity, we used them to predict responses to Cartesian gratings, non-Cartesian gratings, natural images, and curved contours. Based on these predictions, we find that the majority of neurons in V1 are selective for Cartesian gratings, whereas the majority of V4 neurons are selective for non-Cartesian gratings or natural images. The SRF describes visual tuning properties with a second-order nonlinear model. These results support the hypothesis that a second-order model is sufficient to describe the general mechanisms mediating shape selectivity in area V4.

Mesh:

Year:  2006        PMID: 16987926     DOI: 10.1152/jn.00575.2006

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  53 in total

1.  Neural coding of image structure and contrast polarity of Cartesian, hyperbolic, and polar gratings in the primary and secondary visual cortex of the tree shrew.

Authors:  Jordan Poirot; Paolo De Luna; Gregor Rainer
Journal:  J Neurophysiol       Date:  2016-02-03       Impact factor: 2.714

2.  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

3.  Trade-off between curvature tuning and position invariance in visual area V4.

Authors:  Tatyana O Sharpee; Minjoon Kouh; John H Reynolds
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-24       Impact factor: 11.205

4.  'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

Authors:  Dean A Pospisil; Anitha Pasupathy; Wyeth Bair
Journal:  Elife       Date:  2018-12-20       Impact factor: 8.140

5.  Estimating linear-nonlinear models using Renyi divergences.

Authors:  Minjoon Kouh; Tatyana O Sharpee
Journal:  Network       Date:  2009       Impact factor: 1.273

6.  Representing "stuff" in visual cortex.

Authors:  Corey M Ziemba; Jeremy Freeman
Journal:  Proc Natl Acad Sci U S A       Date:  2015-01-13       Impact factor: 11.205

7.  Image statistics underlying natural texture selectivity of neurons in macaque V4.

Authors:  Gouki Okazawa; Satohiro Tajima; Hidehiko Komatsu
Journal:  Proc Natl Acad Sci U S A       Date:  2014-12-22       Impact factor: 11.205

8.  Modeling diverse responses to filled and outline shapes in macaque V4.

Authors:  Dina V Popovkina; Wyeth Bair; Anitha Pasupathy
Journal:  J Neurophysiol       Date:  2019-01-30       Impact factor: 2.714

9.  Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.

Authors:  Marino Pagan; Eero P Simoncelli; Nicole C Rust
Journal:  Neural Comput       Date:  2016-09-14       Impact factor: 2.026

10.  Coding of shape from shading in area V4 of the macaque monkey.

Authors:  Fabrice Arcizet; Christophe Jouffrais; Pascal Girard
Journal:  BMC Neurosci       Date:  2009-11-30       Impact factor: 3.288

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.