A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons

PLoS Computational Biology

Volumn 8, Issue 3, 2012, Pages

  Vavoulis, Dimitrios V ^a   Straub, Volko A ^b   Aston, John A D ^a   Feng, Jianfeng ^a,c  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

^c FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COST FUNCTIONS; COVARIANCE MATRIX; DYNAMICAL SYSTEMS; EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS; NEURONS; OPTIMIZATION; STATE SPACE METHODS;

BIOPHYSICAL MODEL; COST-FUNCTION; HODGKIN-HUXLEY-TYPE MODELS; INFERENCE PROBLEM; MODELING APPROACH; NEURON NETWORKS; PARAMETERS ESTIMATION; SELF-ORGANIZING STATE SPACE MODELS; SINGLE NEURON; TRADITIONAL APPROACHES;

PARAMETER ESTIMATION;

CALCIUM CHANNEL N TYPE; POTASSIUM; SODIUM;

ACCURACY; ARTICLE; CALCIUM CELL LEVEL; CALCIUM CURRENT; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; HODGKIN HUXLEY EQUATION; ION CURRENT; KINEMATICS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL MEMBRANE CONDUCTANCE; NERVE CELL NETWORK; NERVE CELL STIMULATION; NERVOUS SYSTEM ELECTROPHYSIOLOGY; POTASSIUM CURRENT; PREDICTION; PROBABILITY; SODIUM CURRENT; STEADY STATE;

EID: 84861128339     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002401     Document Type: Article

References (46)

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, Szücs 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, Schürmann F, Markram H, et al. (2007) A novel multiple objective optimization framework for constraining conductance-based 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 parameter-search 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. pp. 251-256 In: Experimental Chaos: 7th Experimental Chaos Conference. volume 676.
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 multi-objective optimization. Evol Comput 15: 1-28.
31. Price KV, Storn RM, Lampinen JA, (2005) Differential evolution: a practical approach to global optimization Springer pp. 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. (2001) 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 pp. 588 p.
41. Vehovszky A, Szabó 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 single-compartment neuron models using a synchronization-based method. Neurocomputing 70: 1605-1610.
44. Huys QJM, Ahrens MB, Paninski L, (2006) Efficient estimation of detailed single-neuron 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.