Literature DB >> 11934454

A model of speed tuning in MT neurons.

John A Perrone1, Alexander Thiele.   

Abstract

We have shown previously that neurons in the middle temporal (MT) area of primate cortex have inseparable spatiotemporal receptive fields-their response profiles exhibit a ridge that is oriented in the spatiotemporal frequency domain, and this orientation predicts the neurons' preferred speed. When measured in spatiotemporal frequency space, such MT spectral receptive field (SRF) properties are closely matched to the spectrum generated by a moving edge. In contrast, V1 neurons have SRF properties that are poorly matched to moving edge spectra, indicating that V1 neurons are not tuned to a particular image speed but rather to specific spatial and temporal frequencies. Here we describe a neural mechanism based directly on the properties of V1 neurons that is able to explain the SRF change that occurs between V1 and MT. We outline the theory behind this transformation and posit an explanation for how the visual system extracts true speed (independent of spatial frequency) from retinal image motion. We tested this speed model against our MT neuron data and found that it provides an excellent account of speed tuning in MT.

Entities:  

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Year:  2002        PMID: 11934454     DOI: 10.1016/s0042-6989(02)00029-9

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  18 in total

1.  Interactions between speed and contrast tuning in the middle temporal area: implications for the neural code for speed.

Authors:  Bart Krekelberg; Richard J A van Wezel; Thomas D Albright
Journal:  J Neurosci       Date:  2006-08-30       Impact factor: 6.167

2.  Aging affects the neural representation of speed in Macaque area MT.

Authors:  Yun Yang; Jie Zhang; Zhen Liang; Guangxing Li; Yongchang Wang; Yuanye Ma; Yifeng Zhou; Audie G Leventhal
Journal:  Cereb Cortex       Date:  2008-11-26       Impact factor: 5.357

3.  Disparity- and velocity-based signals for three-dimensional motion perception in human MT+.

Authors:  Bas Rokers; Lawrence K Cormack; Alexander C Huk
Journal:  Nat Neurosci       Date:  2009-07-05       Impact factor: 24.884

4.  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

5.  A Model of Binocular Motion Integration in MT Neurons.

Authors:  Pamela M Baker; Wyeth Bair
Journal:  J Neurosci       Date:  2016-06-15       Impact factor: 6.167

6.  Two mechanisms for optic flow and scale change processing of looming.

Authors:  Finnegan J Calabro; Kunjan D Rana; Lucia M Vaina
Journal:  J Vis       Date:  2011-03-08       Impact factor: 2.240

7.  Visual motion aftereffects arise from a cascade of two isomorphic adaptation mechanisms.

Authors:  Alan A Stocker; Eero P Simoncelli
Journal:  J Vis       Date:  2009-08-24       Impact factor: 2.240

8.  Adaptation to one perceived motion direction can generate multiple velocity aftereffects.

Authors:  Nikos Gekas; Pascal Mamassian
Journal:  J Vis       Date:  2021-05-03       Impact factor: 2.240

9.  Spectral receptive field properties of neurons in the feline superior colliculus.

Authors:  Wioletta J Waleszczyk; Attila Nagy; Marek Wypych; Antal Berényi; Zsuzsanna Paróczy; Gabriella Eördegh; Anaida Ghazaryan; György Benedek
Journal:  Exp Brain Res       Date:  2007-03-13       Impact factor: 2.064

10.  Performance characterization of Watson Ahumada motion detector using random dot rotary motion stimuli.

Authors:  Siddharth Jain
Journal:  PLoS One       Date:  2009-02-19       Impact factor: 3.240

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