Smoothing of, and parameter estimation from, noisy
Comp. Bio. 5(5): e1000379.
Background: 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
Methodology: We extend previous techniques to take the noisy nature of
the measurements into account. Sequential Monte Carlo 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 nonparametric manner.
Conclusions / Significance: 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. 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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