Uncertainty quantification and sensitivity analysis of a hippocampal CA3 pyramidal neuron model under electromagnetic induction

Ghori, Muhammad Bilal; Kang, Yanmei

doi:10.1007/s11071-023-08514-7

Cited by 1 publication

(4 citation statements)

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“…Investigating the extent to which changes in the system’s equilibrium depend on input parameter uncertainty could be an interesting future exploration. In this context, a potential development might involve conducting a bifurcation analysis, similar to the approach in Ghori and Kang 2023 [ 23 ], with the value of the stimulus current, I stim , serving as a tunable parameter.…”

Section: Discussionmentioning

confidence: 99%

“…We adopted the assumption that all parameters under consideration for the sensitivity analysis followed a uniform distribution [ 23 ] within a predefined percentage range centered around their nominal values. For this reason a ‘hyper-parameter’ was introduced to control the uncertainty across all parameters, as in Pathmanathan et al 2019 [ 24 ]: where P i ,nom is the nominal value of P i .…”

Section: Methodsmentioning

confidence: 99%

“…Due to the rapid dynamics of ϕ msn , conducting an uncertainty quantification and sensitivity analysis at every time-step might not yield meaningful results. In scenarios like this, a common practice is to identify a few key scalar spiking features as relevant QoIs, as seen in previous works such as Pathmanathan et al 2019 [ 24 ] and Ghori and Kang 2023 [ 23 ], and compute the Total Order Sobol’ indices for each of them. Consequently, in the physiological case we have identified the following three main features to serve as our outputs:…”

Section: Methodsmentioning

confidence: 99%

“…Despite the prevalence of such uncertainty, deterministic models assigning single numerical values to each parameter are common, and UQ and GSA have, to our knowledge, been overlooked or approached with simplistic one-at-a-time methods [19]. Understanding how uncertain parameters affect model outputs is crucial for extracting meaningful insights from the models [22][23][24]. Moreover, UQ and GSA serve as valuable tools to investigate channelopathies by revealing how modulations in specific ion channels or transporters influence neural activity.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty quantification and sensitivity analysis of neuron models with ion concentration dynamics

Signorelli,

Manzoni,

Sætra

2024

PLoS ONE

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This paper provides a comprehensive and computationally efficient case study for uncertainty quantification (UQ) and global sensitivity analysis (GSA) in a neuron model incorporating ion concentration dynamics. We address how challenges with UQ and GSA in this context can be approached and solved, including challenges related to computational cost, parameters affecting the system’s resting state, and the presence of both fast and slow dynamics. Specifically, we analyze the electrodiffusive neuron-extracellular-glia (edNEG) model, which captures electrical potentials, ion concentrations (Na+, K+, Ca2+, and Cl−), and volume changes across six compartments. Our methodology includes a UQ procedure assessing the model’s reliability and susceptibility to input uncertainty and a variance-based GSA identifying the most influential input parameters. To mitigate computational costs, we employ surrogate modeling techniques, optimized using efficient numerical integration methods. We propose a strategy for isolating parameters affecting the resting state and analyze the edNEG model dynamics under both physiological and pathological conditions. The influence of uncertain parameters on model outputs, particularly during spiking dynamics, is systematically explored. Rapid dynamics of membrane potentials necessitate a focus on informative spiking features, while slower variations in ion concentrations allow a meaningful study at each time point. Our study offers valuable guidelines for future UQ and GSA investigations on neuron models with ion concentration dynamics, contributing to the broader application of such models in computational neuroscience.

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Section: Discussionmentioning

confidence: 99%