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A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

By Dimitrios V. Vavoulis, Volko A. Straub, John A. D. Aston and Jianfeng Feng


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

Topics: Research Article
Publisher: Public Library of Science
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Provided by: PubMed Central

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  1. (1999). A comparative survey of automated parametersearch methods for compartmental neural models.
  2. (2007). A novel multiple objective optimization framework for constraining conductancebased neuron models by experimental data.
  3. (2007). A parameter-space search algorithm tested on a hodgkinhuxley model.
  4. (1998). A self-organizing state-space model.
  5. (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons.
  6. (1999). An improved parameter estimation method for hodgkin-huxley models.
  7. (2007). An overview of existing methods and recent advances in sequential monte carlo.
  8. (2008). Automated neuron model optimization techniques: a review.
  9. (2010). Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: a computational study.
  10. (2005). Biophysics of computation: information processing in single neurons
  11. (1997). Compartmental model of vertebrate motoneurons for ca2+- dependent spiking and plateau potentials under pharmacological treatment.
  12. (2006). Complex parameter landscape for a complex neuron model.
  13. (2001). Computational Neuroscience: realistic modelling for experimentalists
  14. (2007). Covariance matrix adaptation for multiobjective optimization.
  15. (2011). Data assimilation using a gpu accelerated path integral monte carlo approach. arXiv :
  16. (2005). Differential evolution: a practical approach to global optimization Springer.
  17. (2006). Efficient estimation of detailed singleneuron models. J Neurophysiol 96: 872–90. Parameter Estimation in Hodgkin-Huxley-Type Models PLoS
  18. (2011). Efficient fitting of conductance-based model neurons from somatic current clamp. J Comput Neurosci. E-pub ahead of print.
  19. (2001). Extrinsic modulation and motor pattern generation in a feeding network: a cellular study.
  20. (2002). Failure of averaging in the construction of a conductance-based neuron model.
  21. (2001). Global structure, robustness, and modulation of neuronal models.
  22. (2005). Korngreen A
  23. (2005). Lansner A
  24. (2008). Minimal hodgkinhuxley type models for different classes of cortical and thalamic neurons.
  25. (2006). Modeling single-neuron dynamics and computations: a balance of detail and abstraction.
  26. (2007). Models wagging the dog: are circuits constructed with disparate parameters?
  27. (2007). Modulation of serotonergic neurotransmission by nitric oxide.
  28. (2007). Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.
  29. (2008). Noise in the nervous system.
  30. (2010). Nonlinear data assimilation in geosciences: an extremely efficient particle filter.
  31. (2005). Octopamine increases the excitability of neurons in the snail feeding system by modulation of inward sodium current but not outward potassium currents.
  32. (2010). Parameter estimation and model selection in computational biology.
  33. (2003). Parameter estimation for neuron models. In:
  34. (2007). Parameter estimation in singlecompartment neuron models using a synchronization-based method.
  35. (2000). Parameter estimation methods for single neuron models.
  36. (1996). Principles of rhythmic motor pattern generation.
  37. (2008). Probing the dynamics of identified neurons with a datadriven modeling approach.
  38. (2004). Similar network activity from disparate circuit parameters.
  39. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings.
  40. (2003). The motor infrastructure: from ion channels to neuronal networks.
  41. (2011). The use of automated parameter searches to improve ion channel kinetics for neural modeling.
  42. (2008). Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions.

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