Literature DB >> 25057148

Spectral receptive fields do not explain tuning for boundary curvature in V4.

Timothy D Oleskiw1, Anitha Pasupathy1, Wyeth Bair2.   

Abstract

The midlevel visual cortical area V4 in the primate is thought to be critical for the neural representation of visual shape. Several studies agree that V4 neurons respond to contour features, e.g., convexities and concavities along a shape boundary, that are more complex than the oriented segments encoded by neurons in the primary visual cortex. Here we compare two distinct approaches to modeling V4 shape selectivity: one based on a spectral receptive field (SRF) map in the orientation and spatial frequency domain and the other based on a map in an object-centered angular position and contour curvature space. We test the ability of these two characterizations to account for the responses of V4 neurons to a set of parametrically designed two-dimensional shapes recorded previously in the awake macaque. We report two lines of evidence suggesting that the SRF model does not capture the contour sensitivity of V4 neurons. First, the SRF model discards spatial phase information, which is inconsistent with the neuronal data. Second, the amount of variance explained by the SRF model was significantly less than that explained by the contour curvature model. Notably, cells best fit by the curvature model were poorly fit by the SRF model, the latter being appropriate for a subset of V4 neurons that appear to be orientation tuned. These limitations of the SRF model suggest that a full understanding of midlevel shape representation requires more complicated models that preserve phase information and perhaps deal with object segmentation.
Copyright © 2014 the American Physiological Society.

Keywords:  computational model; macaque monkey; object recognition; shape processing; ventral visual pathway

Mesh:

Year:  2014        PMID: 25057148      PMCID: PMC4274922          DOI: 10.1152/jn.00250.2014

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


  28 in total

1.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT.

Authors:  D H HUBEL; T N WIESEL
Journal:  J Neurophysiol       Date:  1965-03       Impact factor: 2.714

2.  Receptive fields of single neurones in the cat's striate cortex.

Authors:  D H HUBEL; T N WIESEL
Journal:  J Physiol       Date:  1959-10       Impact factor: 5.182

Review 3.  Distributed hierarchical processing in the primate cerebral cortex.

Authors:  D J Felleman; D C Van Essen
Journal:  Cereb Cortex       Date:  1991 Jan-Feb       Impact factor: 5.357

4.  Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey.

Authors:  J L Gallant; C E Connor; S Rakshit; J W Lewis; D C Van Essen
Journal:  J Neurophysiol       Date:  1996-10       Impact factor: 2.714

5.  Visuotopic organization and extent of V3 and V4 of the macaque.

Authors:  R Gattass; A P Sousa; C G Gross
Journal:  J Neurosci       Date:  1988-06       Impact factor: 6.167

6.  Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form.

Authors:  R Desimone; S J Schein
Journal:  J Neurophysiol       Date:  1987-03       Impact factor: 2.714

7.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex.

Authors:  E Kobatake; K Tanaka
Journal:  J Neurophysiol       Date:  1994-03       Impact factor: 2.714

8.  Selectivity for polar, hyperbolic, and Cartesian gratings in macaque visual cortex.

Authors:  J L Gallant; J Braun; D C Van Essen
Journal:  Science       Date:  1993-01-01       Impact factor: 47.728

9.  Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys.

Authors:  M Mishkin; L G Ungerleider
Journal:  Behav Brain Res       Date:  1982-09       Impact factor: 3.332

10.  The fine structure of shape tuning in area V4.

Authors:  Anirvan S Nandy; Tatyana O Sharpee; John H Reynolds; Jude F Mitchell
Journal:  Neuron       Date:  2013-06-19       Impact factor: 17.173

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  9 in total

1.  '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

2.  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

Review 3.  Visual Functions of Primate Area V4.

Authors:  Anitha Pasupathy; Dina V Popovkina; Taekjun Kim
Journal:  Annu Rev Vis Sci       Date:  2020-06-24       Impact factor: 6.422

Review 4.  Integration of objects and space in perception and memory.

Authors:  Charles E Connor; James J Knierim
Journal:  Nat Neurosci       Date:  2017-10-26       Impact factor: 24.884

Review 5.  Object shape and surface properties are jointly encoded in mid-level ventral visual cortex.

Authors:  Anitha Pasupathy; Taekjun Kim; Dina V Popovkina
Journal:  Curr Opin Neurobiol       Date:  2019-10-04       Impact factor: 6.627

6.  Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Authors:  Ramanujan Srinath; Alexandriya Emonds; Qingyang Wang; Augusto A Lempel; Erika Dunn-Weiss; Charles E Connor; Kristina J Nielsen
Journal:  Curr Biol       Date:  2020-10-22       Impact factor: 10.834

7.  Clustered functional domains for curves and corners in cortical area V4.

Authors:  Rundong Jiang; Ian Max Andolina; Ming Li; Shiming Tang
Journal:  Elife       Date:  2021-05-17       Impact factor: 8.140

8.  Linear and non-linear properties of feature selectivity in V4 neurons.

Authors:  Jon Touryan; James A Mazer
Journal:  Front Syst Neurosci       Date:  2015-05-27

9.  Joint coding of shape and blur in area V4.

Authors:  Timothy D Oleskiw; Amy Nowack; Anitha Pasupathy
Journal:  Nat Commun       Date:  2018-01-31       Impact factor: 14.919

  9 in total

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