Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters ...

Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters ...

Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters ...

We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important ...

Model-based smoothing. A: Data; generated by adding Gaussian noise (σO = 30 mV) to the voltage trace and subsampling every seven timesteps (Δ = 0.02 ms and Δ s ...

Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings · List of references · Publications that cite this publication.

Smoothing of, and parameter estimation from, noisy biophysical recordings (Huys & Paninski 2009) · Overview · Files · Citations.

Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various ...

Sep 10, 2020 · Quentin J. M. Huys , Liam Paninski: Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. PLoS Comput. Biol.

Huys, Q. & Paninski, L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLOS Computational Biology 5: e1000379.