Observers for canonic models of neural oscillators

TL;DR

This paper develops methods for state and parameter estimation in nonlinear neural oscillators, overcoming limitations of traditional observer forms by using parameter-dependent transformations and a novel Lyapunov-based approach.

Contribution

It introduces a new approach for reconstructing states and parameters in neural oscillators, including models not in adaptive observer canonical form, using parameter-dependent transformations and a hybrid Lyapunov method.

Findings

Partial reconstruction of Hindmarsh-Rose and FitzHugh-Nagumo models achieved.

Parameter-dependent coordinate transformations enable observer canonical form.

New method effectively estimates states and parameters in nonlinear models.

Abstract

We consider the problem of state and parameter estimation for a wide class of nonlinear oscillators. Observable variables are limited to a few components of state vector and an input signal. The problem of state and parameter reconstruction is viewed within the classical framework of observer design. This framework offers computationally-efficient solutions to the problem of state and parameter reconstruction of a system of nonlinear differential equations, provided that these equations are in the so-called adaptive observer canonic form. We show that despite typical neural oscillators being locally observable they are not in the adaptive canonic observer form. Furthermore, we show that no parameter-independent diffeomorphism exists such that the original equations of these models can be transformed into the adaptive canonic observer form. We demonstrate, however, that for the class of Hindmarsh-Rose and FitzHugh-Nagumo models, parameter-dependent coordinate transformations can be used to render these systems into the adaptive observer canonical form. This allows reconstruction, at least partially and up to a (bi)linear transformation, of unknown state and parameter values with exponential rate of convergence. In order to avoid the problem of only partial reconstruction and to deal with more general nonlinear models in which the unknown parameters enter the system nonlinearly, we present a new method for state and parameter reconstruction for these systems. The method combines advantages of standard Lyapunov-based design with more flexible design and analysis techniques based on the non-uniform small-gain theorems. Effectiveness of the method is illustrated with simple numerical examples.