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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
Published on: May 10, 2012
Bifurcation analysis applied to a model of motion integration with a multistable stimulus
James Rankin1, Emilien Tlapale, Romain Veltz
1Neuromathcomp Project Team, INRIA Sophia Antipolis Méditerranée, 2004 Route des Lucioles-BP 93, 06902 Sophia Antipolis, France. james.rankin@inria.fr
Journal of Computational Neuroscience
|August 9, 2012
Summary
This study models motion perception for ambiguous visual stimuli. The computational model explains how the brain integrates visual cues to perceive motion, linking different perceptions to distinct model states.
Area of Science:
- Computational neuroscience
- Psychophysics
- Visual perception
Background:
- Motion perception can be ambiguous, especially with stimuli like drifting gratings viewed through apertures.
- The brain integrates contour and terminator cues to resolve visual motion direction.
- Previous models have not fully captured the dynamic interplay leading to multistable percepts.
Purpose of the Study:
- To develop a dynamical computational model of motion integration for a multistable psychophysical stimulus.
- To link different motion percepts to coexisting steady states within the model.
- To investigate parameter space using bifurcation analysis and numerical continuation.
Main Methods:
- Developed a dynamical model for motion direction selection.
- Employed bifurcation analysis and numerical continuation to explore model dynamics.
- Systematically varied model parameters under biological and mathematical constraints.
Main Results:
- Identified a parameter region where the model replicates experimental observations of motion perception.
- Linked distinct percepts (drifting vs. aperture-aligned motion) to coexisting steady states.
- Analyzed temporal dynamics and the effect of stimulus gain.
Conclusions:
- The model successfully captures the qualitative behavior of motion perception for ambiguous stimuli.
- Coexisting steady states provide a framework for understanding multistable percepts.
- Varying stimulus gain offers a way to make qualitative predictions about perceptual dynamics.
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