Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

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

Open Access Poster presentation

Genetic algorithm modification to speed up parameter fitting for a multicompartment neuron model

Ruben A Tikidji-Hamburyan

Author Affiliations

A.B. Kogan Research Institute for Neurocybernetics, Southern Federal University, Rostov – on – Don, Russia

BMC Neuroscience 2008, 9(Suppl 1):P90  doi:10.1186/1471-2202-9-S1-P90

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/9/S1/P90


Published:11 July 2008

© 2008 Tikidji-Hamburyan; licensee BioMed Central Ltd.

Poster presentation

The parameter space of neural multi-compartment models is a non-linear complex multidimensional space. Therefore, the problem of parameter fitting for such models is an optimization problem which searches for a solution point in a surface with ravines and mountains. Recent studies [1-4] showed that a genetic algorithm (GA) effectively solves such complex problems. However, usually a GA needs to generate several millions individuals for a good fitting. If the simulation of each individual requires four seconds for fitness evaluation, the full time for performing an optimization procedure grows to 46 days.

Here a description of a GA modification (MGA) allowing a speeding up of parameter fitting is presented. The use of two types of reproduction operators implements both combinatorial and continual searches. The individual mutation factor (IMF) and second order limitation operator (SOL) proposed in this study allow the algorithm to avoid a capture of the population by local attractors and also reduce the number of generations required for the optimization procedure. The results of preliminary tests and limitations of the proposed method are discussed.

Acknowledgements

The research is supported by UBTec LTD http://www.ubtec.ru and Sun Microsystems http://www.sun.com.

References

  1. Vanier MC, Bower JM: A comparative survey of automated parameter-search methods for compartmental neural models.

    J Comput Neurosci 1999, 7:149-171. PubMed Abstract | Publisher Full Text OpenURL

  2. Murase K, Mochizuki T, Kikuchi T, Ikezoe J: Kinetic parameter estimation from compartment models using a genetic algorithm.

    Nucl Med Commun 1999, 20(10):925-932. PubMed Abstract OpenURL

  3. Keren N, Peled N, Korngreen A: Constraining compartmental models using multiple voltage recordings and genetic algorithms.

    J Neurophysiol 2005, 94:3730-3742. PubMed Abstract | Publisher Full Text OpenURL

  4. Weaver CM, Wearne SL: The role of action potential shape and parameter constraints in optimization of compartment models.

    Neurocomputing 2006, 69:1053-1057. Publisher Full Text OpenURL