Adaptive observers for biophysical neuronal circuits
arXiv:2111.02176 · doi:10.1109/TAC.2023.3344723
Abstract
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.
16 pages. The Julia code used in this paper can be found in https://github.com/thiagoburghi/online-learning