System identification of biophysical neuronal models

Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers
Pages6180-6185
Number of pages6
ISBN (Electronic)978-1-7281-7447-1
DOIs
Publication statusPublished - 11 Jan 2021
Event59th IEEE Conference on Decision and Control (CDC 2020) - Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020
Conference number: 59
https://cdc2020.ieeecss.org/

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference59th IEEE Conference on Decision and Control (CDC 2020)
Abbreviated titleCDC
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period14/12/2018/12/20
Internet address

Fingerprint

Dive into the research topics of 'System identification of biophysical neuronal models'. Together they form a unique fingerprint.

Cite this