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research article

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

Sprekeler, Henning  
•
Wiskott, Laurenz
2011
Neural Computation

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.

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Type
research article
DOI
10.1162/NECO_a_00072
Web of Science ID

WOS:000286133600001

Author(s)
Sprekeler, Henning  
Wiskott, Laurenz
Date Issued

2011

Publisher

Massachusetts Institute of Technology Press

Published in
Neural Computation
Volume

23

Start page

303

End page

335

Subjects

Natural Images

•

Visual-Cortex

•

Statistics

•

Orientation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCN  
Available on Infoscience
December 16, 2011
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/74522
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