Is slowness a learning principle of visual cortex?
” Laurenz Wiskott Institute for Theoretical Biology Humboldt University Berlin 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, scaling, illumination and other transformations can be learned in an unsupervised fashion based on SFA [1]. 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 early visual areas [2]. 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. [1] Wiskott, L. and Sejnowski, T.J.
