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Predicting the Nonlinear Dynamics of Biological Neurons using Support Vector Machines with Different Kernels

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84035

Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Frontzek, T., Lal, T., & Eckmiller, R. (2001). Predicting the Nonlinear Dynamics of Biological Neurons using Support Vector Machines with Different Kernels. In Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC (pp. 1492-1497).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E378-0
Abstract
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian, polynomial and tanh-kernels to learn and predict the nonlinear dynamics of single biological neurons. We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian crayfish, and we determine the optimal SVM parameters with regard to the test error. Compared to conventional RBF networks and MLPs, SVMs with gaussian kernels learned faster and performed a better iterated one-step-ahead prediction with regard to training and test error. From a biological point of view SVMs are especially better in predicting the most important part of the dynamics, where the membranpotential is driven by superimposed synaptic inputs to the threshold for the oscillatory peak.