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Neuronal excitability and parameter variability in the Hodgkin-Huxley model
1The Leslie and Susan Gonda Interdisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.
Plos Computational Biology
|June 29, 2026
Summary
This study reframes the Hodgkin-Huxley neuron model, incorporating parameter uncertainty to reveal a diverse population of firing behaviors. It shows that neuronal excitability arises from complex parameter interactions, not a single set of values.
Area of Science:
- Computational Neuroscience
- Biophysics
- Systems Neuroscience
Background:
- Neuron models traditionally reduce complex data to single parameter sets, ignoring experimental variability.
- This simplification obscures how robustness and degeneracy arise in excitable systems.
Purpose of the Study:
- To reintroduce parameter uncertainty into the Hodgkin-Huxley model.
- To analyze the impact of this uncertainty on neuronal excitability and firing patterns using global sensitivity analysis.
Main Methods:
- Digitized Hodgkin-Huxley rate-constant data and used bootstrap resampling for parameter uncertainty.
- Employed large-scale Monte Carlo simulations on a squid axon cable model.
- Utilized first-order and total-order Sobol sensitivity indices to analyze parameter contributions.
Main Results:
- Simulations generated a heterogeneous population of firing behaviors (non-firing, phasic, regular, spontaneous).
- The canonical Hodgkin-Huxley parameters represented a minority subpopulation, not a unique solution.
- Neuronal excitability was primarily governed by strong interactions among all parameters, indicated by large total-order Sobol indices.
Conclusions:
- The Hodgkin-Huxley model should be viewed as an ensemble of behaviors constrained by experimental data.
- Physiologically relevant firing patterns emerge from specific regions within the parameter space.
- Parameter interactions, not individual parameters, are key drivers of neuronal excitability.
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