System identification of biophysical neuronal models

TL;DR

This paper introduces a novel method for identifying nonlinear neuronal dynamics using Generalized Orthonormal Basis Functions and neural networks, demonstrated on a crab neuron model.

Contribution

It proposes a new operator-based approach with GOBFs for neuronal system identification, addressing ultra-sensitivity issues in nonlinear dynamics.

Findings

GOBFs effectively model neuronal behaviors

The method successfully identifies a bursting crab neuron model

Heuristic for GOBF pole selection improves robustness

Abstract

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.