  The FitzHugh-Nagumo (FHN) model, from computational neuroscience, has
attracted attention in nonlinear dynamics studies as it describes the behavior
of excitable systems and exhibits interesting bifurcation properties. The
accurate estimation of the model parameters is vital to understand how the
solution trajectory evolves in time. To this end, we provide a forward
sensitivity method (FSM) approach to quantify the main model parameters using
sparse measurement data. FSM constitutes a variational data assimilation
technique which integrates model sensitivities into the process of fitting the
model to the observations. We analyse the applicability of FSM to update the
FHN model parameters and predict its dynamical characteristics. Furthermore, we
highlight a few guidelines for observations placement to control the shape of
the cost functional and improve the parameter inference iterations.
