Volume 10 Supplement 1
Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models
© Lepora et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Estimation of the maximal ion channel conductances in Hodgkin-Huxley models from patch clamp data is a difficult optimization problem. Automating this process promises high-throughput computational modeling of use to both experimenters (for rapid feedback on their experimental preparations) and modelers (for investigating the details of neuronal function). Hitherto, attempts to do this have focused on stochastic searches such as genetic algorithms and simulated annealing [1–3]. Such methods give robust estimates of model parameters but converge slowly or need to sample a large population of test cases in parallel, and therefore require substantial computing resources. Meanwhile, deterministic searches (e.g. the simplex search and conjugate gradient descent) are far more computationally efficient but are hampered by the complex fitting landscape of the optimization problem. As such, there is no general neuronal parameter-fitting algorithm that is both computationally efficient and robust.
Almost all methods for neuronal parameter estimation have been based on minimizing the error between model and target membrane potentials. However, many of the neuronal parameters are linear in a residual current error Ires between model and data , which simplifies the optimization problem. Building on this observation, we present a novel deterministic technique for estimating the maximal conductances G, of multi-compartment neurons with active dendrites. Mathematically, our method iteratively looks for roots of Ires(G) = 0, at which the residual current is zero using methods including Newton-Raphson.
Two types of simulated current-clamp data were used for method validation: (i) spiking data from simple (few channels) neuronal models; and (ii) subthreshold data from complicated (many channels) neuronal models. The search typically converged in relatively few iterations (~102 compared to >104 for stochastic methods) and was more robust than searches such as the simplex algorithm, which require a localized search space near to the target values.
Discussion and conclusion
Our novel search technique has been validated against a variety of model-derived data and, on the examples we have considered, shows efficient and robust neuronal parameter fitting. Work is underway to examine performance on more complex spiking models and preliminary findings indicate that phase plane methods [2, 5] may be helpful in this respect. Our aim is to incorporate this work into the CARMEN neuroinformatics infrastructure as a research tool for use by the general neuroscience community.
N. Lepora was supported by EPSRC through the CARMEN e-Science Project.
- Vanier MC, Bower JM: A comparative survey of automated parameter-search methods for compartmental neural models. J Comput Neurosci. 1999, 7: 149-171. 10.1023/A:1008972005316.PubMedView ArticleGoogle Scholar
- Van Geit W, Achard P, de Schutter E: Neurofitter: A parameter tuning package for a wide range of electrophysiological neuron models. Front Neuroinformatics. 2007, 1: 1-18.PubMed CentralView ArticleGoogle Scholar
- 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. Front Neuroscience. 2007, 1: 7-18. 10.3389/neuro.01.1.1.001.2007.View ArticleGoogle Scholar
- Morse TM, Hines ML: Parameter space reduction in neuron model optimization through minimization of residual voltage clamp current. Soc Neurosci Abstr. 2001, 606.5Google Scholar
- LeMasson G, Maex R: Introduction to equation solving and parameter fitting. Computational neuroscience: Realistic Modeling for Experimentalists. Edited by: de Schutter E. CRC Press, London, 1-21.
This article is published under license to BioMed Central Ltd.