System identification of biophysical neuronal models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2020
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| Acceso en línea: | |
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| _version_ | 1866911785437626368 |
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| author | Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe |
| author_facet | Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_07691 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | System identification of biophysical neuronal models Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe Neurons and Cognition Machine Learning Systems and Control 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. |
| title | System identification of biophysical neuronal models |
| topic | Neurons and Cognition Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2012.07691 |