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Global search metaheuristics for neural mass model calibration.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Methods for model calibration ↔ NMMSO_example.m, lines 3–63 · score 0.71 · niching migratory multi, genetic algorithm, swarm optimiser, Rit model, Jansen, simulated
  2. [2] § Methods › Methods for model calibration ↔ Related_scripts/NMMSO_normalised_exit_early.m, the whole file · a weak match · score 0.69 · multi modal, swarm optimisation, multi swarm, particle, population, locations
  3. [3] § Methods › Methods for model calibration ↔ Related_scripts/NMMSO_normalised_iterative.m, lines 1–85 · score 0.67 · multi modal, swarm optimisation, multi swarm, particle, locations, space
  4. [4] § Methods › Methods for model calibration ↔ Related_scripts/NMMSO_normalised_exit_early.m, the whole file · a weak match · score 0.60 · niching migratory multi, swarm optimiser, hypercube, space, NMMSO, algorithms
  5. [5] § Methods › Methods for model calibration ↔ ABC_example.jl, lines 167–203 · score 0.59 · ABC SMC, schedule, distance, particles, tolerance, PSD
  6. [6] § Methods › Objective function and tolerance threshold ↔ Related_scripts/Fitness_TF.m, the whole file · a weak match · score 0.51 · squared error, model PSD, sum
  7. [7] § Methods › Objective function and tolerance threshold ↔ Related_scripts/Fitness_TF_inverted.m, the whole file · a weak match · score 0.51 · squared error, model PSD, sum

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 120 lines · 4.8 KB · MIT · 2 matches

  1. function [X,Y,mode_loc_before,mode_y_before,evaluations_before,nmmso_state, ...
  2. mode_loc_after,mode_y_after,evaluations_after] = NMMSO_normalised_exit_early( ...
  3. swarm_size, problem_func,problem_function_params, max_evaluations, ...
  4. mn,mx,max_evol,tol_val,fitness_threshold)
  8. % Implementation of the Niching Migratory Multi-Swarm Optimser, described
  9. % in:
  10. % "Running Up Those Hills: Multi-Modal Search with the Niching Migratory
  11. % Multi-Swarm Optimiser"
  12. % by Jonathan E. Fieldsend
  13. % published in Proceedings of the IEEE Congress on Evolutionary Computation,
  14. % pages 2593-2600, 2014
  15. % Please reference this paper if you undertake work utilising this code.
  16. % Implementation (c) by Jonathan Fieldsend, University of Exeter, 2014
  17. %
  18. % Thanks to Zhan Dawei for identifying the error in the tol_val check
  19. %
  20. % Assumes function maximisation
  21. %
  22. % REQUIRED ARGUMENTS
  23. %
  24. % swarm_size = maximum number of elements (particles) per swarm
  25. % problem_func = string containing name of function to be optimised
  26. % problem_funcion_params = meta-parameters needed by problem function
  27. % (distinct from optimisation (design) parameters
  28. % max_evaluations = maximum number of evaluations to be taken through the
  29. % problem function
  30. % mn = minimum design parameter values (a vector with param_num elements)
  31. % mx = maximum design parameter values (a vector with param_num elements)
  32. %
  33. % OPTIONAL ARGUMENTS
  34. %
  35. % max_evols = maximum number of swarms to update in a generation. If not
  36. % provided this is set at 100
  37. % tol_val = tolerance value for merging automatically (default 10^-6)
  38. %
  39. % OUTPUTS
  40. %
  41. % Due to the algorithm design (dynamic populations) the final generation
  42. % may exceed the alloted maximum number of evaluations. As such the final
  43. % peak estimate state and the penultimate peak estimate state are returned
  44. % (with the corresponding evaluation number tracked). Apart from X and Y,
  45. % other data stored are in the normalised range
  46. %
  47. %
  48. % X = mode locations (in original range),note that at least one is likely
  49. % to be very poor due to the new swarm spawning at the end of each
  50. % generation, and that these will be a combination of both global and
  51. % local mode estimate
  52. % Y = mode values;
  53. % mode_loc_before = design space location of penultimate mode estimates (swarm
  54. % gbests), note that at least one is likely to be very poor due to the
  55. % new swarm spawning at the end of each generation, and that these will
  56. % be a combination of both global and local mode estimate
  57. % mode_y_before = function evalutions corresponding to the mode estimates
  58. % evaluations_before = number of problem function evaluations at end of
  59. % penultimate generation
  60. % mode_loc_after = design space location of mode estimates at end
  61. % mode_y_after = function evalutions corresponding to the mode estimates
  62. % evaluations_after = number of problem function evaluationsat end
  63. %
  64. % nmmso_state = structure holding the state of the swarms. Unless you want
  65. % to pick apart the details of how the algorithm searchs the space, then
  66. % the only two elements you will probably be interested in are X and Y
  67. % which are preallocated matrices to hold all normalised locations visited
  68. % (therefore nmmso_state.X(1:evaluations,:) will hold all the design
  69. % space locations visited by the optimiser thus far. The final version is
  70. % returned (can be quite large)
  71. %
  72. % NOTE: all locations are normalised to the unit hypercube using the box
  73. % constraints in mx and mn -- you will want to rescale back woth mn and mx
  74. % to get the original decision vectors. X and Y hold mode estimated
  75. % locations in original ranges
  76. %
  77. %
  80. if exist('max_evol','var')==0
  81. display('default max_eval used, set at 100');
  82. max_evol=100;
  83. end
  85. if max_evol<=0
  86. display('Max_eval cannot be negative or zero, default max_eval used, set at 100');
  87. max_evol=100;
  88. end
  90. if exist('tol_val','var') ==0
  91. tol_val = 10^-6;
  92. end
  95. % at start no evaluations used, and NMMSO state is empty
  96. mode_loc_after=[];
  97. mode_y_after=[];
  98. evaluations_after=[];
  99. nmmso_state=[];
  100. evaluations_after=0;
  102. % exit algorithm either when max_evaluations has been reached or when we have a mode with a value less than the desired threshold.
  103. fit_val_best = 100;
  104. while ((evaluations_after < max_evaluations)&&(fit_val_best>=fitness_threshold))
  105. mode_loc_before=mode_loc_after;
  106. mode_y_before=mode_y_after;
  107. evaluations_before=evaluations_after;
  108. [mode_loc_after,mode_y_after,evaluations_after,nmmso_state] = NMMSO_normalised_iterative( ...
  109. swarm_size, problem_func,problem_function_params, max_evaluations, ...
  110. mn,mx,evaluations_after,nmmso_state,max_evol,tol_val);
  112. fit_val_best = min(100-mode_y_after);
  114. end
  116. [n,~] = size(mode_loc_after);
  117. X = mode_loc_after.*repmat(mx-mn,n,1)+repmat(mn,n,1);
  118. Y = mode_y_after;

NMMSO_normalised_exit_early.m at commit a8e4edb, under MIT · at the source

Overview

Authors: Dominic M. Dunstan1,2, Mark P. Richardson3, Jonathan E. Fieldsend4, Marc Goodfellow1,2
ORCID iDs: Dominic M. Dunstan, Mark P. Richardson, Marc Goodfellow
  1. Department of Mathematics and Statistics, University of Exeter, Exeter, United Kingdom
  2. Living Systems Institute, University of Exeter, Exeter, United Kingdom
  3. Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  4. Department of Computer Science, University of Exeter, Exeter, United Kingdom
Institutions: University of Exeter (United Kingdom); King's College London (United Kingdom)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1249
Dates: received 29 August 2025; accepted 20 April 2026; published online 5 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1249 · PMID 42266203 · PMCID PMC13245216 · OpenAlex W4413421016
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.18.670825
Status: code verified
Categories: EEG (modality), none (in silico) (organism), computational (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Single-unit activity, calcium imaging, Machine learning
Keywords: neural mass model, EEG, evolutionary search metaheuristics, approximate Bayesian computation, model calibration, parameter inference
Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Engineering and Physical Sciences Research Council (EP/T017856/1, 2407565, UKRI1460); UK Medical Research Council (MR/K013998/1)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Neural mass models (NMMs) are often used to help understand the circuitry that underpins observed brain dynamics in basic and clinical research. A key step is to fuse models with data so that model parameter values can be inferred for a given data set—a process called model fitting or model calibration. This can shed light on putative physiological mechanisms underlying the observed signals. Calibration is notoriously challenging in biology since models are often non-identifiable, high-dimensional, and nonlinear. Established methods such as dynamic causal modelling (DCM) circumvent some of these issues, for example, by incorporating prior information and employing fast local search methods in the space of feasible parameter values (“parameter space”). However, it is pertinent to better understand the potential limitations of these methods so that we can increase our confidence in the use of models to interpret brain activity, and to develop new approaches as required. Here, we use tools from dynamical systems theory to illustrate some of the complexities of model calibration in an archetypal NMM. We use this information to motivate the use of calibration methods that work across large regions of parameter space, rather than focusing on informative priors or localised search methods. We subsequently evaluate the performance of approximate Bayesian computation (ABC) and evolutionary search metaheuristics (ESMs) for mapping feasible sets of parameters for which an NMM can recreate electroencephalographic recordings during an eyes-closed resting state. Our results demonstrate the superiority of ESMs in terms of computational efficiency and accuracy. Furthermore, we elucidate potential reasons why ESMs are able to perform better than ABC, that is, that they are less susceptible to biases induced by the complexity of underlying cost landscapes. These results highlight the importance of incorporating ESMs in future efforts to model brain dynamics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

domdunstan/Global_NMM_parameter_inference

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a8e4edb0114e47ab359c07037b0314e19d8dca16, 13 January 2026
Languages: MATLAB (9), Julia (1)
Size: 14 files, 10 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (2 files), DifferentialEquations.jl (1 file), Distributions.jl (1 file), Statistics and Machine Learning Toolbox (1 file), Plots.jl (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/domdunstan/Global_NMM_parameter_inference

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data and Code Availability

Code supporting the findings is publicly available and maintained as a GitHub repository (https://github.com/domdunstan/Global_NMM_parameter_inference). The full EEG dataset is publicly available (https://osf.io/f2vya), with the processed power spectral data available within the GitHub repository.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 2 funders, 57 references.

Cite

This paper

Dunstan, D. M., Richardson, M. P., Fieldsend, J. E., & Goodfellow, M. (2026). Global search metaheuristics for neural mass model calibration. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1249. https://doi.org/10.1162/imag.a.1249

BibTeX

@article{dunstan2026global,
author = {Dunstan, Dominic M. and Richardson, Mark P. and Fieldsend, Jonathan E. and Goodfellow, Marc},
title = {{Global search metaheuristics for neural mass model calibration}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1249},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1249},
url = {https://doi.org/10.1162/imag.a.1249},
pmid = {42266203},
pmcid = {PMC13245216}
}

RIS

TY - JOUR
AU - Dunstan, Dominic M.
AU - Richardson, Mark P.
AU - Fieldsend, Jonathan E.
AU - Goodfellow, Marc
TI - Global search metaheuristics for neural mass model calibration
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/05
VL - 4
SP - IMAG.a.1249
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1249
UR - https://doi.org/10.1162/imag.a.1249
LA - en
ER -

CSL-JSON

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