Implicit Wiener Series
2003
Technical Report
ei
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): | Empirical Inference |
Bibtex Type: | Technical Report (techreport) |
Institution: | Max Planck Institute for Biological Cybernetics |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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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}, doi = {}, month_numeric = {6} } |