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. |
| Links: | |
| Number (issue): | 114 |
| Year: | 2003 |
| Month: | June |
| Day: | 0 |
| BibTeX Type: | Technical Report (techreport) |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@techreport{2291,
title = {Implicit Wiener Series},
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.},
number = {114},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics},
school = {Biologische Kybernetik},
month = jun,
year = {2003},
author = {Franz, MO. and Sch{\"o}lkopf, B.},
month_numeric = {6}
}