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 |
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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 | |
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/ |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2020-December |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 59th IEEE Conference on Decision and Control, CDC 2020 |
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Abbreviated title | CDC |
Country/Territory | Korea, Republic of |
City | Virtual, Jeju Island |
Period | 14/12/20 → 18/12/20 |
Internet address |