System identification of biophysical neuronal models

Thiago B. Burghi; Maarten Schoukens; Rodolphe Sepulchre

doi:10.1109/CDC42340.2020.9304363

System identification of biophysical neuronal models

Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	2020 59th IEEE Conference on Decision and Control, CDC 2020
Publisher	Institute of Electrical and Electronics Engineers
Pages	6180-6185
Number of pages	6
ISBN (Electronic)	978-1-7281-7447-1
DOIs	https://doi.org/10.1109/CDC42340.2020.9304363
Publication status	Published - 11 Jan 2021
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual/Online, Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 Conference number: 59 https://cdc2020.ieeecss.org/

Conference

Conference	59th IEEE Conference on Decision and Control, CDC 2020
Abbreviated title	CDC
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	14/12/20 → 18/12/20
Internet address	https://cdc2020.ieeecss.org/

Funding

*The research leading to these results has received funding from the Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES) – Brasil (Finance Code 001) and the European Research Council (under the Advanced ERC Grant Agreement Switchlet n.670645).

Funders	Funder number
European Union's Horizon 2020 - Research and Innovation Framework Programme	670645
H2020 European Research Council
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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10.1109/CDC42340.2020.9304363

Cite this

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title = "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.",

author = "Burghi, {Thiago B.} and Maarten Schoukens and Rodolphe Sepulchre",

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booktitle = "2020 59th IEEE Conference on Decision and Control, CDC 2020",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "59th IEEE Conference on Decision and Control, CDC 2020, CDC ; Conference date: 14-12-2020 Through 18-12-2020",

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Burghi, TB, Schoukens, M & Sepulchre, R 2021, System identification of biophysical neuronal models. in 2020 59th IEEE Conference on Decision and Control, CDC 2020., 9304363, Institute of Electrical and Electronics Engineers, pp. 6180-6185, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 14/12/20. https://doi.org/10.1109/CDC42340.2020.9304363

System identification of biophysical neuronal models. / Burghi, Thiago B.; Schoukens, Maarten; Sepulchre, Rodolphe.
2020 59th IEEE Conference on Decision and Control, CDC 2020. Institute of Electrical and Electronics Engineers, 2021. p. 6180-6185 9304363.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Schoukens, Maarten

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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Y2 - 14 December 2020 through 18 December 2020

ER -

System identification of biophysical neuronal models

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