Motion Clouds

Snapshot of a Motion Cloud

Motion Clouds are random, textured dynamical stimuli synthesized such as to challenge spatio-temporal integration properties of the early visual system. Unlike classical low-entropy stimuli such as gratings, these stimuli are less susceptible to create interference patterns when mixed together. This is essential to study integrative and discriminative properties of the low-level sensory systems. Moreover, this pseudo-random stimulation protocol allows to make a trial-by-trial analysis locked to the stimulation onset. This allows to study experimentally trial-by-trial variability and relative importance between measurement noise and contextual uncertainty.

This is a first step before extending synthesis to probabilistic synthesis models of the texture’s geometric structure. The model will use geometrical multi-scale transformations extending the classical wavelet representation. For instance, these transformations synthesize the stimuli as randomized superposition of geometrical wavelets that match the spatio-temporal profile of association fields in V1. These will be implemented by computing evolutions of partial differential equations with randomized initial conditions. Finally, models are designed such that we explicitly tune the statistics of the generative model and thus control the structural complexity of the stimuli, such as different scales of smoothness in the spatio-temporal dynamics as displayed by natural scenes.

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Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

Publications

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it …

Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures

A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on …

Beyond simply faster and slower: exploring paradoxes in speed perception

Estimating object speed in visual scenes is a critical part of perception. While various aspects of speed computation including …

Dynamic Textures For Probing Motion Perception

This work extends the MotionClouds dynamic texture model testing aspects of its parametrization with an application in psychophysics.

How and why do image frequency properties influence perceived speed?

Humans are able to interact successfully with moving objects in our dynamic world and the visual system effi ciently performs the …

Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception

The visual system does not process information instantaneously, but rather integrates over time. Integration occurs both for stationary …

Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional …

Effect of image statistics on fixational eye movements

Under natural viewing conditions, small movements of the eyes prevent the maintenance of a steady direction of gaze. It is unclear how …

More is not always better: dissociation between perception and action explained by adaptive gain control

Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal …

Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize

In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …