Parallel Tempering MCMC on Liu et al 1998 (Wang et al 2022)

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"... we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets..."
1 . Wang YC, Rudi J, Velaso J, Sinha N, Idumah G, Powers RK, Heckman CJ, Chardon MK (2022) Multimodal parameter spaces of a complex multi-channel neuron model Front. Systems Neurosci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I A; I Calcium; I K,Ca; I_K,Na;
Gap Junctions:
Simulation Environment: Python;
Model Concept(s):
Search NeuronDB for information about:  I A; I K,Ca; I Calcium; I_K,Na;
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