System identification of biophysical neuronal models
Abstract
After sixty years of quantitative biophysical modeling of neurons, the identification of neuronal dynamics from input-output data remains a challenging problem, primarily due to the inherently nonlinear nature of excitable behaviors. By reformulating the problem in terms of the identification of an operator with fading memory, we explore a simple approach based on a parametrization given by a series interconnection of Generalized Orthonormal Basis Functions (GOBFs) and static Artificial Neural Networks. We show that GOBFs are particularly well-suited to tackle the identification problem, and provide a heuristic for selecting GOBF poles which addresses the ultra-sensitivity of neuronal behaviors. The method is illustrated on the identification of a bursting model from the crab stomatogastric ganglion.
Cite
@article{arxiv.2012.07691,
title = {System identification of biophysical neuronal models},
author = {Thiago B. Burghi and Maarten Schoukens and Rodolphe Sepulchre},
journal= {arXiv preprint arXiv:2012.07691},
year = {2024}
}
Comments
Slightly extended pre-print of the paper to be presented at the 59th Conference on Decision and Control, held remotely between December 14-18, 2020