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      Modelling Adaptation to Directional Motion Using the Adelson-Bergen Energy Sensor

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      1 , 2 , * , 1 , 3 , 4
      PLoS ONE
      Public Library of Science

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          Abstract

          The motion energy sensor has been shown to account for a wide range of physiological and psychophysical results in motion detection and discrimination studies. It has become established as the standard computational model for retinal movement sensing in the human visual system. Adaptation effects have been extensively studied in the psychophysical literature on motion perception, and play a crucial role in theoretical debates, but the current implementation of the energy sensor does not provide directly for modelling adaptation-induced changes in output. We describe an extension of the model to incorporate changes in output due to adaptation. The extended model first computes a space-time representation of the output to a given stimulus, and then a RC gain-control circuit (“leaky integrator”) is applied to the time-dependent output. The output of the extended model shows effects which mirror those observed in psychophysical studies of motion adaptation: a decline in sensor output during stimulation, and changes in the relative of outputs of different sensors following this adaptation.

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          Most cited references20

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          Normalization of cell responses in cat striate cortex.

          D J Heeger (1992)
          Simple cells in the striate cortex have been depicted as half-wave-rectified linear operators. Complex cells have been depicted as energy mechanisms, constructed from the squared sum of the outputs of quadrature pairs of linear operators. However, the linear/energy model falls short of a complete explanation of striate cell responses. In this paper, a modified version of the linear/energy model is presented in which striate cells mutually inhibit one another, effectively normalizing their responses with respect to stimulus contrast. This paper reviews experimental measurements of striate cell responses, and shows that the new model explains a significantly larger body of physiological data.
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            Directionally selective complex cells and the computation of motion energy in cat visual cortex.

            We applied a set of 1- and 2-bar tests to directionally selective (DS) complex cells in the cat's striate cortex, and compared the responses with those predicted by two computational models. Single-bar responses and 2-bar interactions produce distinctive patterns that are highly diagnostic. The observed responses are quite similar to those predicted by a basic (non-opponent) motion-energy model [Adelson & Bergen (1985) Journal of the Optical Society of America A, 2, 284-299]. However, they are not consistent with an opponent combination of energy models, nor are they consistent with any stage of the classic Reichardt model. In particular, the Reichardt model (as well as opponent combinations of energy models) predicts a separable space-time symmetry in the 2-bar interaction that is not observed in our measurements, while the non-opponent energy model predicts an inseparable, oriented interaction very similar to the measured cortical responses. Comparisons between model and measurements suggest possible mechanisms of spatial receptive-field organization and of nonlinear transformations.
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              Transparent motion perception as detection of unbalanced motion signals. II. Physiology.

              We investigated how the primate visual system solves the difficult problem of representing multiple motion vectors in the same part of the visual space--the problem of motion transparency. In the preceding companion article we reported that displays with locally well-balanced motion signals in opposite directions are perceptually nontransparent (i.e., one does not see two coherent moving surfaces) and that transparent displays always contain locally unbalanced motion signals. This is exemplified by our paired and unpaired dot patterns. Although both types of stimuli contain two sets of dots moving in opposite directions, the former is locally well balanced and appears like flicker while the latter gives a perception of two transparent surfaces. In this article we report our physiological recordings from areas V1 and MT of behaving monkeys, comparing single-cell responses to the paired and the unpaired dot patterns. Although a small proportion of directionally selective V1 cells responded differently to the two types of patterns, the average V1 responses could not reliably distinguish between the paired and the unpaired stimuli. A large fraction of MT cells, on the other hand, responded significantly better to the unpaired dot patterns than to the paired ones. Furthermore, the average response of all MT cells to the unpaired dot patterns was significantly higher than that to the paired dot patterns. These results demonstrate a neural correlate of the perceptual transparency at the level of MT. On the other hand, V1 cells do not generally discriminate between the transparent and nontransparent stimuli, indicating that V1 activity is not well correlated with the perception of motion transparency. Our results are consistent with a two-stage model for motion processing: the first stage measures local motion and the second stage introduces suppression if different directions of motion are present at a local region of the visual field. The first stage is located primarily in V1 and the second stage primarily in MT. Finally, we found a strong and negative correlation between the degree of the opponent-direction suppression of MT cells and their responses to flicker noise stimuli. This result suggests that one of the fundamental roles of the opponent-direction suppression in MT is noise reduction.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                15 March 2013
                : 8
                : 3
                : e59298
                Affiliations
                [1 ]SISSA, Trieste, Italy
                [2 ]Institut für Psychologie, Universität Regensburg, Regensburg, Germany
                [3 ]PRISMA Cluster of Excellence & Institute of Physics (THEP), University of Mainz, Mainz, Germany
                [4 ]School of Psychology, University of Lincoln, Lincoln, United Kingdom
                Brain and Spine Institute (ICM), France
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AP AC GM. Performed the experiments: AP AC. Analyzed the data: AC AP. Contributed reagents/materials/analysis tools: AP AC. Wrote the paper: AP AC GM.

                Article
                PONE-D-12-18210
                10.1371/journal.pone.0059298
                3598751
                23555013
                e48f93d9-7c91-403e-a091-aaea63c38ccc
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 June 2012
                : 14 February 2013
                Page count
                Pages: 7
                Funding
                This research was supported by SISSA (International School for Advanced Studies), the Alexander von Humboldt Foundation, The University of Mainz, the University of Lincoln, and the Wellcome Trust. AC was also supported by the Deutsche Forschungsgemeinschaft (DFG) within the Emmy-Noether program (Grant SA/1975 1-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Computational Neuroscience
                Circuit Models
                Coding Mechanisms
                Sensory Systems
                Neuroscience
                Sensory Systems
                Visual System
                Sensory Perception
                Social and Behavioral Sciences
                Psychology
                Psychophysics

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                Uncategorized

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