Feedback Identification of conductance-based models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866907799397597184 |
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| author | Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe |
| author_facet | Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe |
| contents | This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise. While the nonlinear system exhibits excitability, bifurcations, and limit-cycle oscillations, we prove consistency of the parameter estimation procedure under output feedback. Hence, this paper provides a rigorous framework for the application of conventional nonlinear system identification methods to discrete-time stochastic neuronal systems. The main result exploits the elementary property that conductance-based models of neurons have an exponentially contracting inverse dynamics. This property is implied by the voltage-clamp experiment, which has been the fundamental modeling experiment of neurons ever since the pioneering work of Hodgkin and Huxley. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2002_09626 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Feedback Identification of conductance-based models Burghi, Thiago B. Schoukens, Maarten Sepulchre, Rodolphe Systems and Control Neurons and Cognition This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise. While the nonlinear system exhibits excitability, bifurcations, and limit-cycle oscillations, we prove consistency of the parameter estimation procedure under output feedback. Hence, this paper provides a rigorous framework for the application of conventional nonlinear system identification methods to discrete-time stochastic neuronal systems. The main result exploits the elementary property that conductance-based models of neurons have an exponentially contracting inverse dynamics. This property is implied by the voltage-clamp experiment, which has been the fundamental modeling experiment of neurons ever since the pioneering work of Hodgkin and Huxley. |
| title | Feedback Identification of conductance-based models |
| topic | Systems and Control Neurons and Cognition |
| url | https://arxiv.org/abs/2002.09626 |