A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed.


Published in:
Neural Computation, 23, 303-335
Year:
2011
Publisher:
Massachusetts Institute of Technology Press
ISSN:
0899-7667
Keywords:
Laboratories:




 Record created 2011-12-16, last modified 2018-01-28


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