ArticlePLoS computational biology2009
Smoothing of, and parameter estimation from, noisy biophysical recordings.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Article
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- Training deep neural density estimators to identify mechanistic models of neural dynamics.eLife · 2020Article
- Estimation of neuron parameters from imperfect observations.PLoS computational biology · 2020Article
- Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.Frontiers in physics · 2020Article
- Data Assimilation Methods for Neuronal State and Parameter Estimation.Journal of mathematical neuroscience · 2018Review
- Using computational theory to constrain statistical models of neural data.Current opinion in neurobiology · 2017Review
- A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.PLoS computational biology · 2017Article
- Is realistic neuronal modeling realistic?Journal of neurophysiology · 2016Review
- Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.Journal of computational neuroscience · 2016Article
- Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance.Current opinion in neurobiology · 2016Review
- BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.Frontiers in neuroinformatics · 2016Article
- Computational models in the age of large datasets.Current opinion in neurobiology · 2015Review
- Beyond the connectome: the dynome.Neuron · 2014Review
- A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.BMC bioinformatics · 2014Article
- Fast state-space methods for inferring dendritic synaptic connectivity.Journal of computational neuroscience · 2014Article
- A flexible, interactive software tool for fitting the parameters of neuronal models.Frontiers in neuroinformatics · 2014Article
- A unified approach to linking experimental, statistical and computational analysis of spike train data.PloS one · 2014Article
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2 authors.
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Abstract
Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering") methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.
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