Literature DB >> 1542059

Model for the computation of self-motion in biological systems.

J A Perrone1.   

Abstract

I present a method by which direction- and speed-tuned cells, such as those commonly found in the middle temporal area of the primate brain, can be used to analyze the patterns of retinal image motion that are generated during observer movement through the environment. For pure translation, the retinal image motion is radial in nature and expands out from a point that corresponds to the direction of heading. This heading direction can be found by the use of translation detectors that act as templates for the radial image motion. Each translation detector sums the outputs of direction- and speed-tuned motion sensors arranged such that their preferred direction of motion lies along the radial direction out from the detector center. The most active detector signifies the heading direction. Rotation detectors can be constructed in a similar fashion to detect areas of uniform image speed and direction in the motion field produced by observer rotation. A model consisting of both detector types can determine the heading direction independently of any rotational motion of the observer. The model can achieve this from the outputs of the two-dimensional motion sensors directly and does not assume the existence of accurate estimates of image speed and direction. It is robust to the aperture problem and is biologically realistic. The basic elements of the model have been shown to exist in the primate visual cortex.

Keywords:  NASA Discipline Space Human Factors; Non-NASA Center

Mesh:

Year:  1992        PMID: 1542059     DOI: 10.1364/josaa.9.000177

Source DB:  PubMed          Journal:  J Opt Soc Am A        ISSN: 0740-3232            Impact factor:   2.129


  24 in total

1.  Receptive field dynamics underlying MST neuronal optic flow selectivity.

Authors:  Chen Ping Yu; William K Page; Roger Gaborski; Charles J Duffy
Journal:  J Neurophysiol       Date:  2010-03-24       Impact factor: 2.714

2.  The temporal dynamics of heading perception in the presence of moving objects.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurophysiol       Date:  2015-10-28       Impact factor: 2.714

3.  Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation.

Authors:  J A Perrone; L S Stone
Journal:  J Neurosci       Date:  1998-08-01       Impact factor: 6.167

4.  Heading perception depends on time-varying evolution of optic flow.

Authors:  Charlie S Burlingham; David J Heeger
Journal:  Proc Natl Acad Sci U S A       Date:  2020-12-16       Impact factor: 11.205

5.  3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code.

Authors:  Michael Beyeler; Nikil Dutt; Jeffrey L Krichmar
Journal:  J Neurosci       Date:  2016-08-10       Impact factor: 6.167

6.  Motion anisotropies and heading detection.

Authors:  M Lappe; J P Rauschecker
Journal:  Biol Cybern       Date:  1995       Impact factor: 2.086

7.  Time-to-passage judgments in nonconstant optical flow fields.

Authors:  M K Kaiser; H Hecht
Journal:  Percept Psychophys       Date:  1995-08

8.  Computing the direction of heading from affine image flow.

Authors:  J M Beusmans
Journal:  Biol Cybern       Date:  1993       Impact factor: 2.086

9.  Human heading judgments in the presence of moving objects.

Authors:  C S Royden; E C Hildreth
Journal:  Percept Psychophys       Date:  1996-08

10.  Head direction is coded more strongly than movement direction in a population of entorhinal neurons.

Authors:  Florian Raudies; Mark P Brandon; G William Chapman; Michael E Hasselmo
Journal:  Brain Res       Date:  2014-11-01       Impact factor: 3.252

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