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Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines

2001

Conference Paper

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Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian 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, SVMs 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 Conference on Artificial Neural Networks (ICANN'2001)
Pages: 390-398
Year: 2001
Day: 0

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

Event Name: Proceedings of the International Conference on Artificial Neural Networks (ICANN’2001)

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

Links: PDF

BibTex

@inproceedings{1936,
  title = {Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines},
  author = {Frontzek, T. and Lal, TN. and Eckmiller, R.},
  journal = {Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001)},
  pages = {390-398},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2001}
}