Redwood Neuroscience
Title "Is slowness a learning
principle of visual cortex?"
Laurenz Wiskott
Institute
for Theoretical Biology
Abstract:
Slow Feature Analysis (SFA) is an algorithm for
extracting slowly varying features from a quickly varying signal. It has been shown in network simulations on
1-dimensional stimuli that visual invariances to
shift, scaling, illumination and other transformations can be learned in an
unsupervised fashion based on SFA [1].
More recently we have shown that SFA applied to image
sequences generated from natural images using a range of spatial
transformations results in units that share many properties of complex and hypercomplex cells of early visual areas [2]. We
find cells responsive to Gabor stimuli with phase
invariance, sharpened or widened orientation or frequency tuning, secondary
response lobes, end-stopping, and cells selective for direction of motion.
These results indicate that slowness may be an
important principle of self-organization in the visual cortex.
[1] Wiskott, L. and Sejnowski, T.J. (2002). Slow Feature Analysis: Unsupervised Learning
of Invariances.
Neural Computation, 14(4):715-770. http://itb1.biologie.hu-berlin.de/~wiskott/Abstracts/WisSej2002.html
[2] Berkes, P. and Wiskott, L.
(2003). Slow feature analysis yields a
rich repertoire of complex-cell properties.
Cognitive Sciences EPrint Archive (CogPrints) 2804, http://cogprints.ecs.soton.ac.uk/archive/00002804/
http://itb1.biologie.hu-berlin.de/~wiskott/Abstracts/BerkWisk2003a.html