Proc. Japan-Germany Symposium on Computational Neuroscience, Wako, Saitama, Japan, February 1-4, publ. RIKEN Brain Science Institute, p. 25, (abstract) (2006-02-01) (bibtex)

Is slowness a learning principle of visual cortex?

Laurenz Wiskott

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.
[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.

Keywords: visual system, invariances, receptive fields, complex cells, slow feature analysis

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February 7, 2006, Laurenz Wiskott,