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...

Full description

Saved in:
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
Subjects
Applied sciences
Artificial intelligence
Biological system modeling
Computer science; control theory; systems
Delay
Exact sciences and technology
Hippocampus
Information processing
Kernel
Mathematical model
Mechanical factors
Parametric statistics
Predictive models
Testing
Input output
Non linear model
Point process
High precision
Dynamic properties
Neural network
Dynamical system
Dynamic model
Modeling
Volterra equation
Quantitative analysis
Input output model
Online AccessGet full text

Cover

More Information
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