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Poster

Modeling the Dynamics of Individual Neurons of the Stomatogastric Networks with Support Vector Machines

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

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

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Zitation

Frontzek, T., Gutzen C, Lal, T., Heinzel H-G, Eckmiller, R., & Böhm, H. (2001). Modeling the Dynamics of Individual Neurons of the Stomatogastric Networks with Support Vector Machines.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E3B4-8
Zusammenfassung
In small rhythmic active networks timing of individual neurons is crucial for generating different spatial-temporal motor patterns. Switching of one neuron between different rhythms can cause transition between behavioral modes. In order to understand the dynamics of rhythmically active neurons we analyzed the oscillatory membranpotential of a pacemaker neuron and used different neural network models to predict dynamics of its time series. In a first step we have trained conventional RBF networks and Support Vector Machines (SVMs) using gaussian kernels with intracellulary recordings of the pyloric dilatator neuron in the Australian crayfish, Cherax destructor albidus. As a rule SVMs were able to learn the nonlinear dynamics of pyloric neurons faster (e.g. 15s) than RBF networks (e.g. 309s) under the same hardware conditions. After training SVMs performed a better iterated one-step-ahead prediction of time series in the pyloric dilatator neuron with regard to test error and error sum. The test error decreased with increasing number of support vectors. The best SVM used 196 support vectors and produced a test error of 0.04622 as opposed to the best RBF with 0.07295 using 26 RBF-neurons. In pacemaker neuron PD the timepoint at which the membranpotential will cross threshold for generation of its oscillatory peak is most important for determination of the test error. Interestingly SVMs are especially better in predicting this important part of the membranpotential which is superimposed by various synaptic inputs, which drive the membranpotential to its threshold.