https://doi.org/10.1140/epjs/s11734-025-01728-3
Regular Article
Improving Izhikevich neuron models and analyzing dynamical behavior through Bayesian inference
1
Department of Physics, College of Sciences, Nanjing Agricultural University, 210095, Nanjing, China
2
Innovation Research Departments, China Design Group Co., Ltd, 210014, Nanjing, China
3
Department of Cyber Security, Henan Police College, 450046, Zhengzhou, China
Received:
14
April
2025
Accepted:
2
June
2025
Published online:
30
June
2025
To address the impact of observational data uncertainty on parameter inversion, this study establishes a mathematical parameter estimation framework for the Izhikevich neuron model based on Bayesian inference. Utilizing experimentally recorded membrane potential time series from biological neurons, Bayesian inference is implemented via the Markov Chain Monte Carlo (MCMC) method. Specifically, an adaptive Metropolis–Hastings (M-H) algorithm is employed, leveraging the covariance matrix of the posterior parameters to estimate the parameter distributions and solve the resulting multi-parameter inverse problem. All prior distributions are assumed to follow independent Gaussian distributions, and the likelihood function is modeled as Gaussian. By analyzing the posterior distributions of the model parameters, the uncertainty of the model is quantified and characterized. The results show that the parameters inferred through Bayesian inference are closer to the actual biological neuron behavior compared to the initial model parameters. Finally, the bifurcation analysis is performed on different types of Izhikevich neuron models using the estimated parameters, and the resulting transitions in firing patterns are found to be more consistent with experimental membrane potential dynamics. This study highlights the potential of Bayesian inference in addressing inverse problems related to the dynamic behavior of biological neurons.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
