Proc. Jahrestagung der Deutschen Zoologischen Gesellschaft 2003, Berlin, June 9-13, special issue of Zoology, 106(4):373-382 (2003) (bibtex)

Is slowness a learning principle of the visual cortex?

Laurenz Wiskott and Pietro Berkes

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 and other transformations can be learned in an unsupervised fashion based on SFA. 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 primary visual cortex.  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.

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

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August 31, 2007, Laurenz Wiskott,