1 code implementation • 8 Sep 2023 • Raphael Schmetterling, Thiago B. Burghi, Rodolphe Sepulchre
The control of neuronal networks, whether biological or neuromorphic, relies on tools for estimating parameters in the presence of model uncertainty.
no code implementations • 9 Sep 2022 • Jin Gyu Lee, Thiago B. Burghi, Rodolphe Sepulchre
This paper stresses the analogy of this question with the classical question of feedback stabilization.
no code implementations • 4 Apr 2022 • Thiago B. Burghi, Timothy O'Leary, Rodolphe Sepulchre
In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models.
1 code implementation • 3 Nov 2021 • Thiago B. Burghi, Rodolphe Sepulchre
This paper presents adaptive observers for online state and parameter estimation of a class of nonlinear systems motivated by biophysical models of neuronal circuits.
no code implementations • 14 Dec 2020 • Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
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.
no code implementations • 22 Feb 2020 • Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
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.
