Adaptive Observers for Biophysical Neuronal Circuits

• 成果类型: Article
• 署名作者: Burghi, Thiago B.; Sepulchre, Rodolphe
• 署名单位: University of Cambridge; KU Leuven
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3344723
• 发表日期: 2024
• 页码: 5020-5033
• 关键词: Ions; Adaptation models; Integrated circuit modeling; observers; Biological system modeling; uncertainty; Adaptive systems; Adaptive observers; conductance-based models; Contraction theory; neuroscience; Nonlinear systems

摘要

This article 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 RLS 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.