Adaptive observers for biophysical neuronal circuits
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866914680394481664 |
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| author | Burghi, Thiago B. Sepulchre, Rodolphe |
| author_facet | Burghi, Thiago B. Sepulchre, Rodolphe |
| contents | This paper presents adaptive observers for online state and parameter estimation of a class of nonlinear systems motivated by biophysical models of neuronal circuits. We first present a linear-in-the-parameters design that solves a classical recursive least squares problem. Then, building on this simple design, we present an augmented adaptive observer for models with a nonlinearly parameterized internal dynamics, the parameters of which we interpret as structured uncertainty. We present a convergence and robustness analysis based on contraction theory, and illustrate the potential of the approach in neurophysiological applications by means of numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_02176 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Adaptive observers for biophysical neuronal circuits Burghi, Thiago B. Sepulchre, Rodolphe Systems and Control Neurons and Cognition This paper presents adaptive observers for online state and parameter estimation of a class of nonlinear systems motivated by biophysical models of neuronal circuits. We first present a linear-in-the-parameters design that solves a classical recursive least squares problem. Then, building on this simple design, we present an augmented adaptive observer for models with a nonlinearly parameterized internal dynamics, the parameters of which we interpret as structured uncertainty. We present a convergence and robustness analysis based on contraction theory, and illustrate the potential of the approach in neurophysiological applications by means of numerical simulations. |
| title | Adaptive observers for biophysical neuronal circuits |
| topic | Systems and Control Neurons and Cognition |
| url | https://arxiv.org/abs/2111.02176 |