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Implicit Wiener Series

2003

Technical Report

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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.

Author(s): Franz, MO. and Schölkopf, B.
Number (issue): 114
Year: 2003
Month: June
Day: 0

Department(s): Empirische Inferenz
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics

Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@techreport{2291,
  title = {Implicit Wiener Series},
  author = {Franz, MO. and Sch{\"o}lkopf, B.},
  number = {114},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2003},
  month_numeric = {6}
}