We describe a framework to generate random texture movies with controlled information content. In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these "Motion Clouds" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation.
Model Type: Connectionist Network
Model Concept(s): Pattern Recognition; Temporal Pattern Generation; Spatio-temporal Activity Patterns; Parameter Fitting; Methods; Perceptual Categories; Noise Sensitivity; Envelope synthesis; Sensory processing; Motion Detection
Simulation Environment: Python
References:
Leon PS, Vanzetta I, Masson GS, Perrinet LU. (2012). Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception. Journal of neurophysiology. 107 [PubMed]