Smoothing of, and parameter estimation from, noisy biophysical recordings (Huys & Paninski 2009)

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Accession:232913
" ... 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-maximisation. ..."
Reference:
1 . Huys QJ, Paninski L (2009) Smoothing of, and parameter estimation from, noisy biophysical recordings. PLoS Comput Biol 5:e1000379 [PubMed]
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Brain Region(s)/Organism:
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Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
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Simulation Environment: MATLAB;
Model Concept(s): Detailed Neuronal Models; Methods; Parameter Fitting;
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