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

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" ... 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. ..."
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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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Detailed Neuronal Models; Methods; Parameter Fitting;
Model-based smoothing of noisy voltage measurements. This code is released in
conjunction with the following publication 

  Huys QJ and Paninski L (2007): Model-based filtering of, and parameter
  estimation from, noisy biophysical observations. 
  (available at

and can be downloaded from

SmoothVoltageData.m is a MATLAB m-file, which containts a few functions. 

It sets up a single-compartment cell with Hodgkin-Huxley channels, adds noise to
the voltage and then and runs a particle smoother on data to recover the
underlying voltage. 

To run it, add this folder to the MATLAB path, type 


and hit return. 

Copyright Quentin Huys 2007 October 1st, 2007 
Center for Theoretical Neuroscience, Columbia University 
Email: qh  uys [at] gat s b y  [dot] u c l . ac. u k 
(just get rid of the spaces, replace [at] with @ and [dot] with .)