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This study presents efficient methods for uncertainty quantification and global sensitivity analysis in neuron models with ion dynamics. It identifies key parameters influencing neuron behavior, crucial for computational neuroscience advancements.

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Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Biophysics

Background:

  • Neuron models with ion concentration dynamics present significant challenges for uncertainty quantification (UQ) and global sensitivity analysis (GSA) due to computational cost and complex dynamics.
  • Existing methods struggle with parameters affecting resting states and the interplay of fast (electrical potentials) and slow (ion concentrations) dynamics.

Purpose of the Study:

  • To develop and apply computationally efficient UQ and GSA methods to a detailed neuron model (edNEG) that includes ion concentration dynamics.
  • To address challenges in UQ/GSA for complex neuron models, including computational expense and parameter influence on resting state and dynamics.
  • To identify key parameters driving neuron model behavior under physiological and pathological conditions.

Main Methods:

  • Utilized a variance-based GSA approach to pinpoint influential input parameters.
  • Employed surrogate modeling and efficient numerical integration to reduce computational burden.
  • Developed a strategy to isolate parameters affecting the neuron's resting state.
  • Analyzed the electrodiffusive neuron-extracellular-glia (edNEG) model, incorporating six compartments and dynamics of key ions (Na+, K+, Ca2+, Cl-) and volume.

Main Results:

  • Successfully quantified uncertainty and identified critical parameters in the edNEG model.
  • Demonstrated the effectiveness of surrogate modeling for computationally intensive UQ and GSA.
  • Characterized the influence of uncertain parameters on both rapid spiking dynamics and slower ion concentration changes.
  • Provided insights into model behavior under physiological and pathological conditions.

Conclusions:

  • The developed UQ and GSA methodology is computationally efficient and applicable to complex neuron models with ion dynamics.
  • The study offers practical guidelines for future research in computational neuroscience, enhancing the reliability and interpretability of neuron models.
  • This work contributes to a deeper understanding of how parameter uncertainty affects neuronal function and dysfunction.