Abstract: Slow Feature Analysis (SFA) is an algorithm for extracting slowly
varying features from a quickly varying signal. We have 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 applied SFA to image sequences generated from
natural images using a range of spatial transformations. The resulting
units share many properties with complex and hypercomplex cells of early
visual areas [2]. All are responsive to Gabor stimuli with phase
invariance, some show sharpened or widened orientation or frequency
tuning, secondary response lobes, end-stopping, or selectivity 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://itb.biologie.hu-berlin.de/~wiskott/Abstracts/WisSej2002.html
[2] Berkes, P. and Wiskott, L. (2005). Slow feature analysis yields a
rich repertoire of complex cell properties. Journal of Vision,
5(6):579-602. http://journalofvision.org/5/6/9/
Keywords: visual system, invariances, receptive fields, complex cells, slow feature analysis