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Cited by 941Visual perception and processing mechanismsAdvanced Vision and ImagingNeural dynamics and brain function

A model of neuronal responses in visual area MT

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Simoncelli, Eero P., Heeger, David J.·1998·Vision Research
DOI 10.1016/s0042-6989(97)00183-1
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Abstract

Electrophysiological studies indicate that neurons in the middle temporal (MT) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented via the distribution of MT neuronal responses. The computation is performed in two stages, corresponding to neurons in cortical areas V1 and MT. Each stage computes a weighted linear sum of inputs, followed by rectification and divisive normalization. V1 receptive field weights are designed for orientation and direction selectivity. MT receptive field weights are designed for velocity (both speed and direction) selectivity. The paper includes computational simulations accounting for a wide range of physiological data, and describes experiments that could be used to further test and refine the model.

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  • Responses of several spatio-temporally separable responses can be combined to encode the local speed and direction of motion of the video sequence.

    “Further, the responses of several spatio-temporally separable responses can be combined to encode the local speed and direction of motion of the video sequence [57], [58].”

    From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [58]

    The MT pattern cells section says the model combines responses from V1 cells to produce selectivity for velocity, which includes speed and direction. But the Spatio-temporal frequency tuning section says the model's V1 responses are not spatially and temporally separable; separable tuning is described as a possible alternative, not a feature of the model. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The models in [58], [72] are rather elaborate, physiologically plausible mechanisms designed to match the properties of visual neurons, while our model is designed from an engineering standpoint of capturing distortions in videos.

    “The models in [58], [72] are rather elaborate, physiologically plausible mechanisms designed to match the properties of visual neurons. Our model is designed from an engineering standpoint of capturing distortions in videos.”

    From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [58]

    The Conclusions describe a two-stage physiological model that accounts for much of the physiology of MT neurons, supporting the claim about matching visual-neuron properties. But the same passage emphasizes its simplicity and small number of free parameters, rather than calling it elaborate. The citing authors’ description of their own video-distortion model is not attributed to this source. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Models for speed tuned neurons have been constructed by combining the outputs of a set of V1 cells whose orientation is consistent with the desired velocity.

    “Models for such speed tuned neurons have been constructed by combining the outputs of a set of V1 cells whose orientation is consistent with the desired velocity [58].”

    From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [58]

  • Translational motion has an easily accessible representation in the frequency domain.

    “Translational motion has an easily accessible representation in the frequency domain and these ideas have been used to build motion estimation algorithms for video [8], [57], [58].”

    From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [58]

  • The temporal quality computation bears several similarities with the neuronal model of MT, including the weighting procedure based on distance between linear filters and the motion plane and the normalization of weighted responses.

    “Our temporal quality computation bears several similarities with the neuronal model of MT in [58], [72]. Similarities include the weighting procedure based on the distance between the linear filters and the motion plane and the normalization of weighted responses.”

    From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [58]

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Authors
Simoncelli, E. P., & Heeger, D. J.
Affiliations
Stanford University (US)
Corresponding author
Simoncelli, Eero P.
ORCID
Venue
Vision Research
Publisher
Elsevier BV
Published
1998-03
Volume
38
Issue
5
Pages
743-761
ISSN
0042-6989 (ISSN-L)
DOI
10.1016/s0042-6989(97)00183-1
OpenAlex
W2063632045
Cited by
941
References
3
Citations by year
2026: 18 · 2025: 25 · 2024: 22 · 2023: 24 · 2022: 32 · 2021: 29 · 2020: 40 · 2019: 28 · 2018: 52 · 2017: 41 · 2016: 49 · 2015: 32 · 2014: 43 · 2013: 40 · 2012: 52
FWCI
16.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Vision Research (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (8920230, 9624855, CAREER)
  • Alfred P. Sloan Foundation
  • University of Pennsylvania
  • National Institutes of Health (5r29mh050228-05)
  • National Institute of Mental Health (MH50228)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
middle temporal area, MT neurons, velocity selectivity, direction selectivity, computational model of visual cortex, divisive normalization, orientation selectivity, motion perception
MeSH terms
Brain Mapping, Humans, Mathematics, Models, Neurological, Motion Perception, Time Factors

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