Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)

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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.
1 . 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. J Neurophysiol 107:3217-26 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: Python;
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;
#!/usr/bin/env python
Testing all possible export types 

(c) Laurent Perrinet - INT/CNRS

import MotionClouds as mc

name = 'export'

fx, fy, ft = mc.get_grids(mc.N_X, mc.N_Y, mc.N_frame)
color = mc.envelope_color(fx, fy, ft)

name_ = mc.figpath + name
z = color * mc.envelope_gabor(fx, fy, ft)
for vext in ['.h5', '.mpg', '.zip', '.mat']:
    if mc.anim_exist(name_, vext=vext):
        mc.anim_save(z, name_, display=False, vext=vext)

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