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A theory of slow feature analysis for transformation-based input signals with an application to complex cells

Sprekeler, H and Wiskott, L (2011) A theory of slow feature analysis for transformation-based input signals with an application to complex cells. Neural Computation, 23. pp. 303-335. ISSN 0899-7667

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Abstract

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. © 2011 Massachusetts Institute of Technology.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:25
Last Modified: 08 Dec 2014 02:19
DOI: 10.1162/NECO_a_00072