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Schlagwörter:
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Zusammenfassung:
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.