English
English
Deutsch
日本語
Help
Privacy Policy
Disclaimer
Include files
Advanced Search
Browse
START
BASKET (0)
Tools
Item
ITEM ACTIONS
EXPORT
Add to Basket
Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_1792421_2
Details
Summary
Implicit Wiener Series
Franz, M., & Schölkopf, B.
(2003).
Implicit Wiener Series
(114).
Item is
Released
show all
hide all
Basic
show
hide
Item Permalink
:
https://hdl.handle.net/11858/00-001M-0000-0013-DC4D-7
Version Permalink
:
https://hdl.handle.net/11858/00-001M-0000-0013-DC4E-5
Genre
:
Report
Files
show Files
Locators
show
Creators
show
hide
Creators
:
Franz, MO
1
, Author
Schölkopf, B
1
, Author
Affiliations
:
1
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
Content
show
hide
Free keywords
:
-
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.
Details
show
hide
Language(s)
:
Dates
:
Date issued:
2003-06
Publication Status
:
Issued
Pages
:
-
Publishing info
:
-
Table of Contents
:
-
Rev. Type
:
-
Identifiers
:
Report Nr.: 114
BibTex Citekey: 2291
Degree
:
-
Event
show
Legal Case
show
Project information
show
Source
show