  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 the inherently nonlinear nature of
excitable behaviors. By reformulating the problem in terms of the
identification of an operator with fading memory, we explore a simple approach
based on a parametrization given by a series interconnection of Generalized
Orthonormal Basis Functions (GOBFs) and static Artificial Neural Networks. We
show that GOBFs are particularly well-suited to tackle the identification
problem, and provide a heuristic for selecting GOBF poles which addresses the
ultra-sensitivity of neuronal behaviors. The method is illustrated on the
identification of a bursting model from the crab stomatogastric ganglion.
