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Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
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Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

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Gradient-free MCMC methods for dynamic causal modelling.

Biswa Sengupta1, Karl J Friston1, Will D Penny1

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK.

Neuroimage
|March 18, 2015
PubMed
Summary
This summary is machine-generated.

We compared four gradient-free Markov chain Monte Carlo (MCMC) samplers for Bayesian inversion. Adaptive MCMC sampling proved most efficient, offering the best balance of independent samples and computational time for neural mass models.

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Last Updated: Apr 16, 2026

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859

Area of Science:

  • Computational Neuroscience
  • Statistical Modeling
  • Machine Learning

Background:

  • Bayesian inversion is crucial for parameter estimation in complex models.
  • Gradient-free Markov chain Monte Carlo (MCMC) samplers are vital when model gradients are unavailable or unreliable.
  • Efficient sampling is key to reducing computational cost in Bayesian inference.

Purpose of the Study:

  • To compare the performance of four gradient-free MCMC samplers: random walk Metropolis, slice-sampling, adaptive MCMC, and population-based MCMC with tempering.
  • To evaluate sampler efficiency based on the number of independent samples generated per unit computational time.
  • To identify the most suitable sampler for Bayesian inversion of a single-node neural mass model.

Main Methods:

  • Implementation and testing of four distinct gradient-free MCMC sampling algorithms.
  • Application of samplers to the Bayesian inversion problem of a single-node neural mass model.
  • Quantitative assessment of sampler performance using metrics of independent samples per unit time.

Main Results:

  • Adaptive MCMC and population-based MCMC samplers demonstrated superior efficiency over random walk Metropolis and slice-sampling for the neural mass model.
  • Adaptive MCMC sampling emerged as the most promising method regarding computational time efficiency.
  • Slice-sampling, while producing the highest number of independent samples, incurred a significant computational cost increase compared to adaptive MCMC.

Conclusions:

  • Adaptive MCMC sampling offers a computationally efficient approach for Bayesian inversion of neural mass models.
  • The choice of MCMC sampler significantly impacts the efficiency and feasibility of Bayesian inference in computational neuroscience.
  • Further research into adaptive and population-based MCMC methods is warranted for complex model applications.