System identification of biophysical neuronal models

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Bassinello Burghi, Thiago  ORCID logo
Schoukens, Maarten 
Sepulchre, Rodolphe  ORCID logo

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.

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59th Conference on Decision and Control
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European Research Council (670645)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) -- Brasil (Finance Code 001), Advanced ERC Grant Agreement Switchlet n.670645