CUED Publications database

System identification of biophysical neuronal models *

Burghi, TB and Schoukens, M and Sepulchre, R (2020) System identification of biophysical neuronal models *. In: UNSPECIFIED pp. 6180-6185..

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 26 Oct 2020 20:07
Last Modified: 02 Sep 2021 05:28
DOI: 10.1109/CDC42340.2020.9304363