Łukasz Paszkowski

TL;DR: The present work explores the accuracy of the classical models (a) and (b) by comparing them to more accurate models available where the potentials inside and outside the neurons are computed simultaneously in a self-consistent scheme.

TL;DR: AMICI as discussed by the authors is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.

TL;DR: AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.

TL;DR: This paper uses Newton’s method - like some previous studies - and develops several improvements to achieve robust convergence and shows that the method works robustly in this setting and achieves a speed up of up to 100 compared to using ODE solves.

TL;DR: A new gradient computation method is proposed that facilitates the parameterization of large-scale models based on steady-state measurements that can be combined with existing gradient computation methods for time-course measurements.