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 .
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 . 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.
 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
 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