Bifurcation analysis applied to a model of motion integration with a multistable stimulus

James Rankin 1 Emilien Tlapale 1 Romain Veltz 1 Olivier Faugeras 1 Pierre Kornprobst 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : A computational study into the motion perception dynamics of a multistable psychophysics stimulus is presented. A diagonally drifting grating viewed through a square aperture is perceived as moving in the actual grating direction or in line with the aperture edges (horizontally or vertically). The different percepts are the product of interplay between ambiguous contour cues and specific terminator cues. We present a dynamical model of motion integration that performs direction selection for such a stimulus and link the different percepts to coexisting steady states of the underlying equations. We apply the powerful tools of bifurcation analysis and numerical continuation to study changes to the model's solution structure under the variation of parameters. Indeed, we apply these tools in a systematic way, taking into account biological and mathematical constraints, in order to fix model parameters. A region of parameter space is identified for which the model reproduces the qualitative behaviour observed in experiments. The temporal dynamics of motion integration are studied within this region; specifically, the effect of varying the stimulus gain is studied, which allows for qualitative predictions to be made.
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Article dans une revue
Journal of Computational Neuroscience, Springer Verlag, 2013, 34 (1), pp.103-124. 〈10.1007/s10827-012-0409-5〉
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https://hal.inria.fr/hal-00845593
Contributeur : Pierre Kornprobst <>
Soumis le : mercredi 17 juillet 2013 - 14:13:53
Dernière modification le : jeudi 3 mai 2018 - 13:32:58

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James Rankin, Emilien Tlapale, Romain Veltz, Olivier Faugeras, Pierre Kornprobst. Bifurcation analysis applied to a model of motion integration with a multistable stimulus. Journal of Computational Neuroscience, Springer Verlag, 2013, 34 (1), pp.103-124. 〈10.1007/s10827-012-0409-5〉. 〈hal-00845593〉

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