System identification of biophysical neuronal models
This work provides an incremental method for system identification of biophysical neuronal models, which is a problem for neuroscientists and computational biologists.
This paper addresses the challenge of identifying nonlinear neuronal dynamics from input-output data by reformulating the problem as identifying an operator with fading memory. They propose an approach using a series interconnection of Generalized Orthonormal Basis Functions (GOBFs) and static Artificial Neural Networks, demonstrating its effectiveness on a bursting model from the crab stomatogastric ganglion.
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.