Neuromodulation is central to the adaptation and robustness of animal nervous systems.
This paper explores the classical paradigm of indirect adaptive control to design …

The coloured noise is ubiquitous in industrial processes. This paper addresses the
identification problem for the nonlinear feedback systems with coloured noise. Firstly, a …

Maximal monotonicity is explored as a generalization of the linear theory of passivity, aiming
at an algorithmic input/output analysis of physical models. The theory is developed for …

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 …

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 …

We propose a framework for surrogate modelling of spiking systems. These systems are
often described by stiff differential equations with high-amplitude oscillations and multi …

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 …

This paper presents an adaptive observer for online state and parameter estimation of a
broad class of biophysical models of neuronal networks. The design closely resembles …

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 …

Obtaining predictive models of a neural system is notoriously challenging. Detailed models
suffer from excess model complexity and are difficult to fit efficiently. Simplified models must …