We discuss the problem of determining unknown fixed parameters and unobserved state
variables in nonlinear models of a dynamical system using observed time series data from …

Traditional approaches to the problem of parameter estimation in biophysical models of
neurons and neural networks usually adopt a global search algorithm (for example, an …

In variational formulations of data assimilation, the estimation of parameters or initial state
values by a search for a minimum of a cost function can be hindered by the numerous local …

We address the problem of estimating parameters of chaotic dynamical systems from a time
series in a situation when some of state variables are not observed and/or the data are very …

In this paper, we revisit the issue of the utility of the FitzHugh-Nagumo (FHN) model for
capturing neuron firing behaviors. It has been noted (eg, see [6]) that the FHN model cannot …

We consider the problem of how to recover the state and parameter values of typical model
neurons, such as Hindmarsh-Rose, FitzHugh-Nagumo, Morris-Lecar, from in-vitro …

A numerical parameter estimation method, based on input-output integro-differential
polynomials in a bounded-error framework is investigated in this paper. More precisely, the …

In exploring networks of neurons, the electrophysiology of the nodes (the neurons) and the
links (synaptic or gap junction connections) provide two of the three essential ingredients for …

We study the use of the FitzHugh-Nagumo (FHN) model for capturing neural spiking. The
FHN model is a widely used approximation of the Hodgkin-Huxley model that has significant …

We present a model-based estimation method to reconstruct the unmeasured membrane
potential of neuronal populations from a single-channel electroencephalographic (EEG) …