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

2001

Conference Paper

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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.

Author(s): Frontzek, T. and Lal, TN. and Eckmiller, R.
Journal: Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC
Volume: 2
Pages: 1492-1497
Year: 2001
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Proceedings of the International Joint Conference on Neural Networks (IJCNN’2001) Washington DC

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{1939,
  title = {Predicting the Nonlinear Dynamics of Biological Neurons using Support Vector Machines with Different Kernels},
  author = {Frontzek, T. and Lal, TN. and Eckmiller, R.},
  journal = {Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC},
  volume = {2},
  pages = {1492-1497},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2001}
}