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

 Download zip file 
Help downloading and running models
Accession:146953
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.
Reference:
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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
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;
Implementer(s):
#!/usr/bin/env python
"""

Exploring the orientation component of the envelope around a grating.

"""

import MotionClouds as mc
import numpy

name = 'grating'

#initialize
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
if mc.anim_exist(name_):
    z = color * mc.envelope_gabor(fx, fy, ft)
    mc.figures(z, name_)

# explore parameters
for sigma_div in [1, 2, 3, 5, 8, 13 ]:
    name_ = mc.figpath + name + '-largeband-B_theta-pi-over-' + str(sigma_div).replace('.', '_')
    if mc.anim_exist(name_):
        z = color * mc.envelope_gabor(fx, fy, ft, B_theta=numpy.pi/sigma_div)
        mc.figures(z, name_)

for div in [1, 2, 4, 3, 5, 8, 13, 20, 30]:
    name_ = mc.figpath + name + '-theta-pi-over-' + str(div).replace('.', '_')
    if mc.anim_exist(name_):
        z = color * mc.envelope_gabor(fx, fy, ft, theta=numpy.pi/div)
        mc.figures(z, name_)

V_X = 1.0
for sigma_div in [1, 2, 3, 5, 8, 13 ]:
    name_ = mc.figpath + name + '-B_theta-pi-over-' + str(sigma_div).replace('.', '_') + '-V_X-' + str(V_X).replace('.', '_')
    if mc.anim_exist(name_):
        z = color * mc.envelope_gabor(fx, fy, ft, V_X=V_X, B_theta=numpy.pi/sigma_div)
        mc.figures(z, name_)

for B_sf in [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.8]:
    name_ = mc.figpath + name + '-B_sf-' + str(B_sf).replace('.', '_')
    if mc.anim_exist(name_):
        z = color * mc.envelope_gabor(fx, fy, ft, B_sf=B_sf)
        mc.figures(z, name_) # ,vext='.zip'

Loading data, please wait...