13 citations · 42 across the 8 of their papers we have counts for
8 papers
Robust online estimation of biophysical neural circuits
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. In this work, we explore the r…
Open-loop contraction design
Jin Gyu Lee, Thiago B. Burghi, Rodolphe Sepulchre
Given a non-contracting trajectory of a nonlinear system, we consider the question of designing an input perturbation that makes the perturbed trajectory contracting. This paper st…
Adaptive Conductance Control
Raphael Schmetterling, Thiago Burghi, Rodolphe Sepulchre
Neuromodulation is central to the adaptation and robustness of animal nervous systems. This paper explores the classical paradigm of indirect adaptive control to design neuromodula…
Distributed online estimation of biophysical neural networks
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. Neural systems learn by adjusting…
Adaptive observers for biophysical neuronal circuits
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. We first pres…
System identification of biophysical neuronal models
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 th…
Feedback for nonlinear system identification
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Motivated by neuronal models from neuroscience, we consider the system identification of simple feedback structures whose behaviors include nonlinear phenomena such as excitability…
Feedback Identification of conductance-based models
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. While the no…