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This article is part of the supplement: Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

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Using Neurofitter to fit a Purkinje cell model to experimental data

Werner Van Geit12* and Erik De Schutter12

Author Affiliations

1 Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-Son, Okinawa, 904-0411, Japan

2 Antwerp Theoretical Neurobiology, Universiteit Antwerpen, Wilrijk, Antwerp, 2610, Belgium

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BMC Neuroscience 2008, 9(Suppl 1):P84  doi:10.1186/1471-2202-9-S1-P84

The electronic version of this article is the complete one and can be found online at:

Published:11 July 2008

© 2008 Van Geit and De Schutter; licensee BioMed Central Ltd.

Poster presentation

The cerebellar Purkinje cell is a highly complex neuron that has different firing behaviors, that contains many different ionic mechanisms and that has a complicated dendritic morphology. Therefore models of this neuron are difficult to hand-tune. We used Neurofitter [1], an automated neuron model parameter search tool, to fit both the passive parameters of a neuron model and the maximal conductances of the ion channels to an experimental data set.

The approach is based on the phase-plane trajectory density method [2] that evaluates the difference between the experimental voltage traces and the model output. Optimization algorithms like Evolution Strategies and Mesh Adaptive Search were used to search the parameter space of the model.

The Neurofitter method was already tested before by fitting the parameters of a Purkinje cell model [3] to output generated by the model itself [4], but now we show results that also use experimental data to fit a new version of the Purkinje cell model with updated kinetics. The traces that were used as goal of the optimization consisted of voltage responses of a Purkinje cell neuron to current steps with different amplitude.


WVG is supported a Research Assistant of FWO-Vlaanderen. Experimental data was provided by Arnd Roth and Michael Häusser, UCL, London, UK.


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    Frontiers in Neuroinformatics 2007, 1:1. Publisher Full Text OpenURL

  2. LeMasson G, Maex R: Introduction to equation solving and parameter fitting. In Computational neuroscience: Realistic modeling for experimentalists. London: CRC Press; 2001:1-23. OpenURL

  3. De Schutter E, Bower JM: An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice.

    J Neurophysiol 1994, 71:375-400. PubMed Abstract | Publisher Full Text OpenURL

  4. Achard P, De Schutter E: Complex parameter landscape for a complex neuron model.

    PLoS Comput Biol 2006, 2:e94. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL