Modeling nonlinear neural dynamics with Volterra-Poisson kernels

A nonparametric quantitative model is introduced that captures the nonlinear dynamic properties of neural systems using input/output data. It is based on the Volterra modeling approach adapted for point-process inputs and outputs. Using input/output data, a model is presented for the CAl region of t...

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Bibliographic Details
Published in2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) Vol. 4; pp. 3219 - 3222 vol.4
Main Authors Courellis, S.H., Gholmieh, G., Marmarelis, V.Z., Berger, T.W.
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
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Summary:A nonparametric quantitative model is introduced that captures the nonlinear dynamic properties of neural systems using input/output data. It is based on the Volterra modeling approach adapted for point-process inputs and outputs. Using input/output data, a model is presented for the CAl region of the hippocampus. The model represents reliably the nonlinear dynamic mapping performed by CAI with high accuracy. Compared to traditional descriptors of nonlinear neural dynamics, the presented model provides a generalized, comprehensive view.
ISBN:0780383591
9780780383593
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.2004.1381193