" ... 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. ..."
Model Concept(s): Detailed Neuronal Models; Methods; Parameter Fitting
Simulation Environment: MATLAB
References:
Huys QJ, Paninski L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLoS computational biology. 5 [PubMed]