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This article is part of the supplement: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access Poster Presentation

A qualitative optimization technique for biophysical neuron models with many parameters

Robert Clewley12* and Mirza Dobric2

Author Affiliations

1 Neuroscience Institute, Georgia State University, Atlanta, GA 30303, USA

2 Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA

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BMC Neuroscience 2010, 11(Suppl 1):P39  doi:10.1186/1471-2202-11-S1-P39

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/11/S1/P39


Published:20 July 2010

© 2010 Clewley and Dobric; licensee BioMed Central Ltd.

Poster Presentation

We present a novel computational technique that enables more efficient optimization of qualitative features in biophysical neural models. We extend the idea of multiple objective optimization for a single comparison modality (such as comparison of individual voltage traces) [1] into multiple modalities. For instance, features may be defined in terms of measurements from different experimental scenarios, and might include not only action potential spike shape characteristics (Cf. [1,2]) but also complex features such as the frequency response curve, and phase response curve (PRC). Multiple fuzzy feature modalities provide additional ways to distinguish solutions that might otherwise appear similarly fit in the restrictive view of a single modality and with crisp measures of fitness. An ill-posed optimization problem with many local minima typically arises from relative insensitivities of some parameters to features in a single modality, or parameter co-variation/modulation [2].

We use an implementation in the PyDSTool software [3] to demonstrate how the multi-modal approach can be prepared and embedded in conventional gradient-based or global optimization algorithms. Among other applications, we discuss the transformation over 25 model parameters of a Hodgkin-Huxley neuron model with a Type I PRC [4] to a Type II PRC, using a goal PRC shape and other periodic trajectory statistics as fuzzy objective modalities.

References

  1. Druckmann S, Banitt Y, Gidon A, Schurmann F, Markram H, Segev I: A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data.

    Frontiers in Neuroscience 2007, 1(1):7-18. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Achard P, De Schutter E: Complex Parameter Landscape for a Complex Neuron Model.

    PLoS Comput Biol 2006, 2(7):e94. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Clewley RH, Sherwood WE, Lamar MD, Guckenheimer JM: PyDSTool: a software environment for dynamical systems modeling. [http://pydstool.sourceforge.net] webcite

    2004.

  4. Rinzel J, Ermentrout GB: Analysis of neural excitability and oscillations. In Methods in Neural Modeling. Edited by C Kock and I Segev. MIT Press, Cambridge; 1989:135-169. OpenURL