Literature DB >> 24133273

Diverse suppressive influences in area MT and selectivity to complex motion features.

Yuwei Cui1, Liu D Liu, Farhan A Khawaja, Christopher C Pack, Daniel A Butts.   

Abstract

Neuronal selectivity results from both excitatory and suppressive inputs to a given neuron. Suppressive influences can often significantly modulate neuronal responses and impart novel selectivity in the context of behaviorally relevant stimuli. In this work, we use a naturalistic optic flow stimulus to explore the responses of neurons in the middle temporal area (MT) of the alert macaque monkey; these responses are interpreted using a hierarchical model that incorporates relevant nonlinear properties of upstream processing in the primary visual cortex (V1). In this stimulus context, MT neuron responses can be predicted from distinct excitatory and suppressive components. Excitation is spatially localized and matches the measured preferred direction of each neuron. Suppression is typically composed of two distinct components: (1) a directionally untuned component, which appears to play the role of surround suppression and normalization; and (2) a direction-selective component, with comparable tuning width as excitation and a distinct spatial footprint that is usually partially overlapping with excitation. The direction preference of this direction-tuned suppression varies widely across MT neurons: approximately one-third have overlapping suppression in the opposite direction as excitation, and many other neurons have suppression with similar direction preferences to excitation. There is also a population of MT neurons with orthogonally oriented suppression. We demonstrate that direction-selective suppression can impart selectivity of MT neurons to more complex velocity fields and that it can be used for improved estimation of the three-dimensional velocity of moving objects. Thus, considering MT neurons in a complex stimulus context reveals a diverse set of computations likely relevant for visual processing in natural visual contexts.

Mesh:

Year:  2013        PMID: 24133273      PMCID: PMC6618522          DOI: 10.1523/JNEUROSCI.0203-13.2013

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  17 in total

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

Authors:  Junxiang Luo; Keyan He; Ian Max Andolina; Xiaohong Li; Jiapeng Yin; Zheyuan Chen; Yong Gu; Wei Wang
Journal:  J Neurosci       Date:  2019-02-18       Impact factor: 6.167

2.  Neuronal Effects of Spatial and Feature Attention Differ Due to Normalization.

Authors:  Amy M Ni; John H R Maunsell
Journal:  J Neurosci       Date:  2019-05-08       Impact factor: 6.167

3.  Sensitivity of neurons in the middle temporal area of marmoset monkeys to random dot motion.

Authors:  Tristan A Chaplin; Benjamin J Allitt; Maureen A Hagan; Nicholas S C Price; Ramesh Rajan; Marcello G P Rosa; Leo L Lui
Journal:  J Neurophysiol       Date:  2017-06-21       Impact factor: 2.714

Review 4.  Suppressive mechanisms in visual motion processing: From perception to intelligence.

Authors:  Duje Tadin
Journal:  Vision Res       Date:  2015-09-02       Impact factor: 1.886

5.  A Neural Model of MST and MT Explains Perceived Object Motion during Self-Motion.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurosci       Date:  2016-08-03       Impact factor: 6.167

6.  Nonlinear computations shaping temporal processing of precortical vision.

Authors:  Daniel A Butts; Yuwei Cui; Alexander R R Casti
Journal:  J Neurophysiol       Date:  2016-06-22       Impact factor: 2.714

7.  Spatially tuned normalization explains attention modulation variance within neurons.

Authors:  Amy M Ni; John H R Maunsell
Journal:  J Neurophysiol       Date:  2017-07-12       Impact factor: 2.714

8.  Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurosci       Date:  2019-11-07       Impact factor: 6.167

9.  A general method to generate artificial spike train populations matching recorded neurons.

Authors:  Samira Abbasi; Selva Maran; Dieter Jaeger
Journal:  J Comput Neurosci       Date:  2020-01-23       Impact factor: 1.621

10.  Inferring Cortical Variability from Local Field Potentials.

Authors:  Yuwei Cui; Liu D Liu; James M McFarland; Christopher C Pack; Daniel A Butts
Journal:  J Neurosci       Date:  2016-04-06       Impact factor: 6.167

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