Literature DB >> 22673324

Shape encoding consistency across colors in primate V4.

Brittany N Bushnell1, Anitha Pasupathy.   

Abstract

Neurons in primate cortical area V4 are sensitive to the form and color of visual stimuli. To determine whether form selectivity remains consistent across colors, we studied the responses of single V4 neurons in awake monkeys to a set of two-dimensional shapes presented in two different colors. For each neuron, we chose two colors that were visually distinct and that evoked reliable and different responses. Across neurons, the correlation coefficient between responses in the two colors ranged from -0.03 to 0.93 (median 0.54). Neurons with highly consistent shape responses, i.e., high correlation coefficients, showed greater dispersion in their responses to the different shapes, i.e., greater shape selectivity, and also tended to have less eccentric receptive field locations; among shape-selective neurons, shape consistency ranged from 0.16 to 0.93 (median 0.63). Consistency of shape responses was independent of the physical difference between the stimulus colors used and the strength of neuronal color tuning. Finally, we found that our measurement of shape response consistency was strongly influenced by the number of stimulus repeats: consistency estimates based on fewer than 10 repeats were substantially underestimated. In conclusion, our results suggest that neurons that are likely to contribute to shape perception and discrimination exhibit shape responses that are largely consistent across colors, facilitating the use of simpler algorithms for decoding shape information from V4 neuronal populations.

Mesh:

Year:  2012        PMID: 22673324      PMCID: PMC3544963          DOI: 10.1152/jn.01063.2011

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


  58 in total

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Journal:  J Physiol       Date:  1978-10       Impact factor: 5.182

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Authors:  Bevil R Conway; Sebastian Moeller; Doris Y Tsao
Journal:  Neuron       Date:  2007-11-08       Impact factor: 17.173

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  17 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.  Neural Coding for Shape and Texture in Macaque Area V4.

Authors:  Taekjun Kim; Wyeth Bair; Anitha Pasupathy
Journal:  J Neurosci       Date:  2019-04-04       Impact factor: 6.167

3.  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

4.  Human V4 Activity Patterns Predict Behavioral Performance in Imagery of Object Color.

Authors:  Michael M Bannert; Andreas Bartels
Journal:  J Neurosci       Date:  2018-03-08       Impact factor: 6.167

5.  Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion.

Authors:  Hannah Choi; Anitha Pasupathy; Eric Shea-Brown
Journal:  Neural Comput       Date:  2018-03-22       Impact factor: 2.026

Review 6.  Neurophysiological considerations for visual implants.

Authors:  Sabrina J Meikle; Yan T Wong
Journal:  Brain Struct Funct       Date:  2021-11-13       Impact factor: 3.270

7.  Contour Curvature As an Invariant Code for Objects in Visual Area V4.

Authors:  Yasmine El-Shamayleh; Anitha Pasupathy
Journal:  J Neurosci       Date:  2016-05-18       Impact factor: 6.167

Review 8.  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

9.  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

10.  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

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