Volterra representation and Wiener-like identification of nonlinear systems: 
scope and limitations

Palm, G; Pöpel, B

doi:10.1017/S0033583500005163

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Volterra representation and Wiener-like identification of nonlinear systems: scope and limitations

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Palm, G
Former Department Structure and Function of Natural Nerve-Net , Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.cambridge.org/core/journals/quarterly-reviews-of-biophysics/article/volterra-representation-and-wienerlike-identification-of-nonlinear-systems-scope-and-limitations/92B3D6FA12920BA4FE0FD66C507E9A70
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Citation

Palm, G., & Pöpel, B. (1985). Volterra representation and Wiener-like identification of nonlinear systems: scope and limitations. Quarterly Reviews of Biophysics, 18(2), 135-164. doi:10.1017/S0033583500005163.

Cite as: https://hdl.handle.net/21.11116/0000-0006-5775-5

Abstract

After the work of Marmarelis & Naka (1972, 1973) in the catfish retina, systems analysis using stochastic stimuli has had a boom in the seventies (e.g. McCann & Marmarelis, 1975; Eckert & Bishop, 1975; French & Wong, 1977; Lipson, 1975; McCann, 1974; Naka, Marmarelis & Chan, 1975; Spekreijse & Reits, 1982; Trimble & Phillips, 1978; Terzuolo et al. 1982). White-noise analysis was considered to be a general tool for investigating nonlinear systems gaining a maximum of information with a minimum of assumptions about the system. The modification of the original Wiener theory (Wiener, 1958; Cameron & Martin, 1947; McKean, 1972) by Lee & Schetzen (1965) made the theory fairly easy to implement into widely available computers and thus accessible to a larger number of experimenters.