Literature DB >> 19143050

Velocity computation in the primate visual system.

David C Bradley1, Manu S Goyal.   

Abstract

Computational neuroscience combines theory and experiment to shed light on the principles and mechanisms of neural computation. This approach has been highly fruitful in the ongoing effort to understand velocity computation by the primate visual system. This Review describes the success of spatiotemporal-energy models in representing local-velocity detection. It shows why local-velocity measurements tend to differ from the velocity of the object as a whole. Certain cells in the middle temporal area are thought to solve this problem by combining local-velocity estimates to compute the overall pattern velocity. The Review discusses different models for how this might occur and experiments that test these models. Although no model is yet firmly established, evidence suggests that computing pattern velocity from local-velocity estimates involves simple operations in the spatiotemporal frequency domain.

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Year:  2008        PMID: 19143050      PMCID: PMC4750498          DOI: 10.1038/nrn2472

Source DB:  PubMed          Journal:  Nat Rev Neurosci        ISSN: 1471-003X            Impact factor:   34.870


  66 in total

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Journal:  J Neurophysiol       Date:  1993-04       Impact factor: 2.714

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Authors:  C D Salzman; W T Newsome
Journal:  Science       Date:  1994-04-08       Impact factor: 47.728

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Authors:  E H Adelson; J A Movshon
Journal:  Nature       Date:  1982-12-09       Impact factor: 49.962

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  20 in total

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Authors:  Sarah McIntyre; Tatjana Seizova-Cajic; Alex O Holcombe
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3.  Approach sensitivity in the retina processed by a multifunctional neural circuit.

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4.  Going with the Flow: The Neural Mechanisms Underlying Illusions of Complex-Flow Motion.

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5.  Efficient spiking neural network model of pattern motion selectivity in visual cortex.

Authors:  Michael Beyeler; Micah Richert; Nikil D Dutt; Jeffrey L Krichmar
Journal:  Neuroinformatics       Date:  2014-07

6.  Synchronized neural input shapes stimulus selectivity in a collision-detecting neuron.

Authors:  Peter W Jones; Fabrizio Gabbiani
Journal:  Curr Biol       Date:  2010-11-04       Impact factor: 10.834

7.  Subspace mapping of the three-dimensional spectral receptive field of macaque MT neurons.

Authors:  Mikio Inagaki; Kota S Sasaki; Hajime Hashimoto; Izumi Ohzawa
Journal:  J Neurophysiol       Date:  2016-05-18       Impact factor: 2.714

8.  A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies.

Authors:  Shinji Nishimoto; Jack L Gallant
Journal:  J Neurosci       Date:  2011-10-12       Impact factor: 6.167

9.  Responses to random dot motion reveal prevalence of pattern-motion selectivity in area MT.

Authors:  Hironori Kumano; Takanori Uka
Journal:  J Neurosci       Date:  2013-09-18       Impact factor: 6.167

10.  The aperture problem in contoured stimuli.

Authors:  David Kane; Peter J Bex; Steven C Dakin
Journal:  J Vis       Date:  2009-09-16       Impact factor: 2.240

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