Vavoulis, Dimitrios V., Straub, Volko A., Aston, John A. D. and Feng, Jianfeng. (2012) A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons. PLoS Computational Biology, Vol.8 (No.3). e1002401. ISSN 1553-7358

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Abstract

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QP Physiology
Divisions:	Faculty of Science > Computer Science Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Parameter estimation, Neurons -- Mathematical models
Journal or Publication Title:	PLoS Computational Biology
Publisher:	PLOS
ISSN:	1553-7358
Date:	1 March 2012
Volume:	Vol.8
Number:	No.3
Page Range:	e1002401
Identification Number:	10.1371/journal.pcbi.1002401
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	European Commission (EC)
Grant number:	213219 (EC)
References:	1. Herz AVM, Gollisch T, Machens CK, Jaeger D (2006) Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314: 80–5. 2. De Schutter E, Ekeberg O, Kotaleski JH, Achard P, Lansner A (2005) Biophysically detailed modelling of microcircuits and beyond. Trends Neurosci 28: 562–9. 3. Grillner S (2003) The motor infrastructure: from ion channels to neuronal networks. Nat Rev Neurosci 4: 573–86. 4. Marder E, Calabrese RL (1996) Principles of rhythmic motor pattern generation. Physiol Rev 76: 687–717. 5. Willms AR, Baro DJ, Harris-Warrick RM, Guckenheimer J (1999) An improved parameter estimation method for hodgkin-huxley models. J Comput Neurosci 6: 145–68. 6. Goldman MS, Golowasch J, Marder E, Abbott LF (2001) Global structure, robustness, and modulation of neuronal models. J Neurosci 21: 5229–38. 7. Golowasch J, Goldman MS, Abbott LF, Marder E (2002) Failure of averaging in the construction of a conductance-based neuron model. J Neurophysiol 87: 1129–31. 8. Prinz AA, Billimoria CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90: 3998–4015. 9. Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7: 1345–52. 10. Nowotny T, Szu¨cs A, Levi R, Selverston AI (2007) Models wagging the dog: are circuits constructed with disparate parameters? Neural Comput 19: 1985–2003. 11. Lepora NF, Overton PG, Gurney K (2011) Efficient fitting of conductance-based model neurons from somatic current clamp. J Comput Neurosci. E-pub ahead of print. 12. Hendrickson EB, Edgerton JR, Jaeger D (2011) The use of automated parameter searches to improve ion channel kinetics for neural modeling. J Comput Neurosci 31: 329–46. 13. Keren N, Bar-Yehuda D, Korngreen A (2009) Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones. J Physiol 587: 1413–37. 14. Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, et al. (2008) Minimal hodgkinhuxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99: 427–41. 15. Hobbs KH, Hooper SL (2008) Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions. J Neurophysiol 99: 1871–83. 16. Nowotny T, Levi R, Selverston AI (2008) Probing the dynamics of identified neurons with a datadriven modeling approach. PLoS One 3: e2627. 17. Reid MS, Brown EA, DeWeerth SP (2007) A parameter-space search algorithm tested on a hodgkinhuxley model. Biol Cybern 96: 625–634. 18. Druckmann S, Banitt Y, Gidon A, Schu¨ rmann F, Markram H, et al. (2007) A novel multiple objective optimization framework for constraining conductancebased neuron models by experimental data. Front Neurosci 1: 7–18. 19. Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2: e94. 20. Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94: 3730–42. 21. Tabak J, Murphey CR, Moore LE (2000) Parameter estimation methods for single neuron models. J Comput Neurosci 9: 215–236. 22. Vanier MC, Bower JM (1999) A comparative survey of automated parametersearch methods for compartmental neural models. J Comput Neurosci 7: 149–71. 23. Van Geit W, De Schutter E, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cybern 99: 241–51. 24. LeMasson G, Maex R (2001) Computational Neuroscience: realistic modelling for experimentalists CRC Press. pp 1–25. 25. Van Geit W, Achard P, De Schutter E (2007) Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models. Front Neuroinform 1: 1. 26. Tokuda I, Parlitz U, Illing L, Kennel M, Abarbanel HD (2003) Parameter estimation for neuron models. In: Experimental Chaos: 7th Experimental Chaos Conference. volume 676. pp 251–256. 27. Lillacci G, Khammash M (2010) Parameter estimation and model selection in computational biology. PLoS Comput Biol 6: e1000696. 28. Huys QJM, Paninski L (2009) Smoothing of, and parameter estimation from, noisy biophysical recordings. PLoS Comput Biol 5: e1000379. 29. Kitagawa G (1998) A self-organizing state-space model. J Am Stat Assoc 93: 1203–1215. 30. Igel C, Hansen N, Roth S (2007) Covariance matrix adaptation for multiobjective optimization. Evol Comput 15: 1–28. 31. Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization Springer. 558 p. 32. Faisal AA, Selen LPJ, Wolpert DM (2008) Noise in the nervous system. Nat Rev Neurosci 9: 292–303. 33. Kloeden PE, Platen E (1999) Numerical Solution of Stochastic Differential Equations Springer. 34. Cappe O, Godsill SJ, Moulines E (2007) An overview of existing methods and recent advances in sequential monte carlo. P IEEE 95: 899–924. 35. Doucet A, de Freitas N, Gordon N, eds. Sequential Monte Carlo Methods in Practice Springer. 36. van Leeuwen PJ (2010) Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Q J R Meteorol Soc 136: 1991–1999. 37. Booth V, Rinzel J, Kiehn O (1997) Compartmental model of vertebrate motoneurons for ca2+- dependent spiking and plateau potentials under pharmacological treatment. J Neurophysiol 78: 3371–85. 38. Straub VA, Benjamin PR (2001) Extrinsic modulation and motor pattern generation in a feeding network: a cellular study. J Neurosci 21: 1767–78. 39. Straub VA, Grant J, O’Shea M, Benjamin PR (2007) Modulation of serotonergic neurotransmission by nitric oxide. J Neurophysiol 97: 1088–99. 40. Koch C (2005) Biophysics of computation: information processing in single neurons Oxford University Press. 588 p. 41. Vehovszky A, Szabo´ H, Elliott CJH (2005) Octopamine increases the excitability of neurons in the snail feeding system by modulation of inward sodium current but not outward potassium currents. BMC Neurosci 6: 70. 42. Vavoulis DV, Nikitin ES, Kemenes I, Marra V, Feng J, et al. (2010) Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: a computational study. Front Behav Neurosci 4: 19. 43. Haufler D, Morin F, C LJ, K SF (2007) Parameter estimation in singlecompartment neuron models using a synchronization-based method. Neurocomputing 70: 1605–1610. 44. Huys QJM, Ahrens MB, Paninski L (2006) Efficient estimation of detailed singleneuron models. J Neurophysiol 96: 872–90. 45. Lee A, Yau C, Giles MB, Doucet A, Holmes CC (2010) On the utility of graphics cards to perform massively parallel simulation of advanced monte carlo methods. J Comput Graph Stat 19: 769–789. 46. Quinn JC, Abarbanel HD (2011) Data assimilation using a gpu accelerated path integral monte carlo approach. arXiv : http://arxiv.org/abs/1103.4887.
URI:	http://wrap.warwick.ac.uk/id/eprint/43324

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