Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Author:

Oesterle JonathanORCID,Krämer Nicholas,Hennig PhilippORCID,Berens PhilippORCID

Abstract

AbstractUnderstanding neural computation on the mechanistic level requires models of neurons and neuronal networks. To analyze such models one typically has to solve coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no, or only a global scalar bound on precision. It can therefore be challenging to estimate the effect of numerical uncertainty on quantities of interest, such as spike-times and the number of spikes. To overcome this problem, we propose to use recently developed sampling-based probabilistic solvers, which are able to quantify such numerical uncertainties. They neither require detailed insights into the kinetics of the models, nor are they difficult to implement. We show that numerical uncertainty can affect the outcome of typical neuroscience simulations, e.g. jittering spikes by milliseconds or even adding or removing individual spikes from simulations altogether, and demonstrate that probabilistic solvers reveal these numerical uncertainties with only moderate computational overhead.

Publisher

Cold Spring Harbor Laboratory

Reference43 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Recent Developments and Resources in Neuroinformatics and Computational Neuroscience;Journal of Biomedical and Sustainable Healthcare Applications;2024-01-05

2. Recent Developments in Neuroinformatics and Computational Neuroscience;Journal of Biomedical and Sustainable Healthcare Applications;2023-07-05

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