Is slowness a learning principle of visual cortex?

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. itb1.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, (accepted). itb1.biologie.hu-berlin.de/~wiskott/Abstracts/BerkWisk2005c.html