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  Implicit Wiener Series: Part I: Cross-Correlation vs. Regression in Reproducing Kernel Hilbert Spaces

Franz, M., & Schölkopf, B.(2003). Implicit Wiener Series: Part I: Cross-Correlation vs. Regression in Reproducing Kernel Hilbert Spaces (114). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DC4D-7 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8BD8-E
Genre: Report

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MPIK-TR-114.pdf (Publisher version), 149KB
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 Creators:
Franz, MO1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a neural system. The classical
estimation method of the expansion coefficients via cross-correlation
suffers from severe problems that prevent its application to
high-dimensional and strongly nonlinear systems. We propose a new
estimation method based on regression in a reproducing kernel Hilbert
space that overcomes these problems. Numerical experiments show
performance advantages in terms of convergence, interpretability and
system size that can be handled.

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 Dates: 2003-06
 Publication Status: Issued
 Pages: 15
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 114
BibTex Citekey: 2291
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 114 Sequence Number: - Start / End Page: - Identifier: -