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

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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:
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Gap Junctions:
Receptor(s):
Gene(s):
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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):
default: test experiments doc
	
report_%.pdf: report_%.py MotionParticles.py
	    pyreport --double $< && open $@

test:
	python test_color.py
	python test_export.py
	python test_grating.py
	python test_radial.py
	python test_speed.py
experiments:
	python experiment_B_sf.py  
	python experiment_competing.py  
	python experiment_smooth.py
	# python experiment_VSDI.py	
figures:
	python fig_artwork_eschercube.py  
	python fig_contrast.py            
wiki:
	python fig_orientation.py
	python fig_ApertureProblem.py     
	python fig_MotionPlaid.py         

doc: 
	@(cd doc && $(MAKE))
	
edit: 
	open Makefile &
	spe &

clean:
	touch *py
	rm -f results/* *.pyc



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