Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity.

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 · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials and methods › Single-compartment membrane model of C. elegans ↔ Supporting_matlab_functions/Conductance_ODE.m, the whole file · a weak match · score 0.81 · synaptic activity variable, leakage potential, reversal potential, Gap junctions, sigmoid, rise
[2] § Materials and methods › Single-compartment membrane model of C. elegans ↔ Supporting_matlab_functions/Run_Model.m, the whole file · a weak match · score 0.80 · synaptic activity variable, leakage potential, reversal potential, sigmoid, rise, width
[3] § Materials and methods › Correlation based functional community detection ↔ Python_codes/draw_subnetwork_from_matlab_data.ipynb, lines 174–228 · score 0.70 · original correlation matrix, Pearson correlation, sorted matrix, MATLAB, block, weighted
[4] § Results › Functional communities (FCs) ↔ Supporting_matlab_functions/wsbm.m, the whole file · a weak match · score 0.68 · edge existence, latent community structures, edge weights, algorithm, WSBM, networks
[5] § Results › Functional communities (FCs) ↔ Main_matlab_codes/fit_wsbm_mode.m, lines 1–122 · score 0.58 · co occurrence matrix, edge weights, algorithm, nodes, WSBM, partitions
[6] § Materials and methods › Correlation based functional community detection ↔ Supporting_matlab_functions/wsbm.m, the whole file · a weak match · score 0.53 · weighted stochastic block, MATLAB, WSBM, model, matrix
[7] § Materials and methods › Correlation based functional community detection ↔ Main_matlab_codes/fit_wsbm.m, the whole file · a weak match · score 0.51 · co occurrence matrix, Pearson correlation, WSBM, Neurons

Paper

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

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The authors' code

MATLAB · 142 lines · 5.9 KB · no license · 2 matches

function [Labels,Model] = wsbm(E,R_Struct,varargin)
%WSBM find latent community structure in weighted networks.
%
% WSBM is the main driver program for finding community structure,
% inferring the vertex-labels and edge-bundle parameters of a
% Weighted Stochastic Block Model (WSBM).
% This algorithm infers the parameters by approximating a posterior
% distribution using an iterative variational Bayes algorithm.
% See Aicher, Jacobs, Clauset (2013) for the theoretical derivation of
% the algorithm
%
% Syntax:
% [Labels] = wsbm(E)
% [Labels] = wsbm(E,k)
% [Labels, Model] = wsbm(...,'ParaName',ParaValue)
%
% Examples:
% Raw_Data = generateEdges();
% % Default
% [Labels] = wsbm(Raw_Data);
% % Infer 2 Groups
% [Labels] = wsbm(Raw_Data,2);
% % Change W_Distr to Exp
% [Labels] = wsbm(Raw_Data,2,'W_Distr','Exp');
% % Run Code in Parallel
% [Labels] = wsbm(Raw_Data,2,'parallel',1);
% % Ignore E_Distr
% [Labels] = wsbm(Raw_Data,2,'E_Distr','None');
% [Labels] = wsbm(Raw_Data,2,'alpha',0);
% % Increase the number of trials
% [Labels] = wsbm(Raw_Data,2,'numTrials','500');
% % Multiple changes at once
% [Labels] = wsbm(Raw_Data,2,'W_Distr','Exp','alpha',0,'parallel',1);
% See WSBMDemo.m for more examples
%
% Inputs:
% E - an m by 3 network edge list (parent,child,weight)
% (If E is an n by n network adjacency matrix, then it will be
% converted into an edge list by ADJ2EDG)
% k - number of blocks (k = 4 default)
%
% Outputs:
% Labels - a n by 1 vector of edge labels (using the MAP estimates)
% - Ties are broken randomly (with a warning message)
% Model - a MATLAB structure for advanced output
%
% For more information, try 'type wsbm.m'
%
% Copyright 2013-2014 Christopher Aicher
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
%
% See also SETUP_DISTR, INSTALLMEXFILES, WSBMDEMO, CALC_LOGEVIDENCE
% WSBM
% Version 1.0 | December 2013 | Christopher Aicher
%
%
% ADVANCED INPUT/OUTPUT:
% -Advanced Inputs: 'ParaName' - ParaValue * indicates default
% --Model Inputs:
% 'W_Distr' - edge-weight distr ('Normal' *)
% 'E_Distr' - edge-existence distr ('Bernoulli' *)
% To select a distribution type it's name:
% Weighted distributions:
% Bernoulli, Binomial, Poisson, Normal, LogNormal,
% Exponential, Pareto, None,
% Edge distributions:
% Bernoulli, Binomial, Poisson, None, DC (Degree Corrected)
% See SETUP_DISTR for more information
% 'R_Struct' - kxk matrix of the block structure.
% 'alpha' - para in [0,1]. 0 only weight, 0.5* both, 1 only existence
% --Inference Options:
% 'numTrials' - number of trials with different random initial conditions
% 'algType' - 'vb'* naive bayes, 'bp' belief propagation
% 'networkType' - 'dir'* directed,'sym' = symmetric,'asym' = asymmetric
% 'nanType' - 'missing'* nans are missing, 'nonedge' = nans are nonedge
% 'mainMaxIter' - Maximum number of iterations in main_loop
% 'mainTol' - Minimum (Max Norm) convergence tolerance in main_loop
% 'muMaxIter' - Maximum number of iterations in mu_loop
% 'muTol' - Minimum (Max Norm) convergence tolerance in mu_loop
% --Extra Options:
% 'verbosity' - 0 silent, 1* default, 2 verbose, 3 very verbose
% 'parallel' - boolean, run in parallel? 0* No (Need Parallel ToolBox)
% 'save' - boolean, save temp results? 0* No
% 'outputpath' - string to where to save temp results (Only if save = 1)
% 'seed' - seed for algtype (mu_0 or mes_0) (Sets numTrials = 1)
% 'mexfile' - boolean, run using MEX files? 1* Yes (Need MEX Files)
% --Prior Options:
% 'mu_0' - kxn matrix prior vector for vertex-label parameters(sums to 1)
%
% -Advanced Outputs:
% 'Model' - struct with the following fields
% 'name' - name of model <W_Distr-E_Distr-R_Struct>
% 'Data' - struct with Data related variables
% 'W_Distr' - struct from SETUP_DISTR
% 'E_Distr' - struct from SETUP_DISTR
% 'R_Struct' - struct with edge-bundle (R) variables
% 'Para' - struct with inferred hyperparamters (tau,mu) and parameter
% estimates (theta)
% 'Options' - struct with inference option information
% 'Flags' - struct with convergence flags
%
%
% For an overview see the README.txt file
%
%-------------------------------------------------------------------------%
% WSBM CODE
%-------------------------------------------------------------------------%
% Call wsbm_driver.m
if nargin > 1,
Model = wsbm_driver(E,R_Struct,varargin{:});
else
Model = wsbm_driver(E);
end
Labels = Model.Para.mu';
[n,k] = size(Labels);
if sum(sum(Labels >= 1/k-10^-3)) > n
fprintf('Breaking %u Ties Randomly\n',sum(sum(Labels >= 1/k-10^-3,2) > 1));
e = [zeros(size(Labels,1),1) cumsum(Labels,2)];
e(:,end) = 1; e(e>1) = 1;
Labels = diff(e,1,2);
Labels = mnrnd(1,Labels);
end
[~,Labels] = max(Labels,[],2);
%-------------------------------------------------------------------------%
% END OF WSBM CODE
%-------------------------------------------------------------------------%
%EOF

wsbm.m at commit 5604e1e, no license · at the source

Overview

Authors: Gourab Kumar Sar¹, Andrew Patton¹, Emma Towlson^1,2,3,4, Jörn Davidsen^1,2

ORCID iDs: Gourab Kumar Sar, Jörn Davidsen

Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada

Institutions: University of Calgary (Canada)

Journal: PLoS computational biology, volume 22, issue 9, article e1014152

Dates: received 20 March 2026; accepted 26 August 2026; published online 15 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pcbi.1014152 · PMID 42743376 · PMCID PMC13588538 · OpenAlex W7213319572

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: C. elegans (organism)

Methods: Connectivity, Single-unit activity, calcium imaging

MeSH: Behavior, Animal*, Caenorhabditis elegans*, Models, Neurological*, Nerve Net*, Neurons*, Animals, Computational Biology, Computer Simulation, Connectome, Electrical Synapses, Locomotion (* major topic)

Journal subjects: Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Psychology, Behavior, Social Sciences, Cell Physiology, Junctional Complexes, Gap Junctions, Anatomy, Nervous System, Synapses, Medicine and Health Sciences, Physiology, Electrophysiology, Neurophysiology, Motor Neurons, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Model Organisms, Caenorhabditis Elegans, Animal Models, Organisms, Eukaryota, Animals, Invertebrates, Nematoda, Caenorhabditis, Zoology, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Brain Mapping, Connectomics, Neuroanatomy

Topic: Genetics, Aging, and Longevity in Model Organisms (Aging, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Natural Sciences and Engineering Research Council of Canada (RGPIN/05221-2020, RGPIN-2021-02949); UCalgary’s VPR Postdoctoral Match-Funding Program; Alberta Graduate Excellence Scholarship; Alberta Innovates Graduate Student Scholarship

Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain sensory circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity.

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.

gourab-sar/C-elegans-synchronization

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 5604e1eb62c16d923835e475ea6b42565bd535ce, 25 February 2026

Languages: MATLAB (20), Jupyter (3)

Size: 31 files, 23 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README, 3 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Matplotlib (3 files), NumPy (3 files), SciPy (3 files), NetworkX (2 files), Optimization Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), pandas (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

24 files

Main_matlab_codes/actual_oscillation.m, MATLAB, 60 lines
Main_matlab_codes/community_detection_matlab.m, MATLAB, 102 lines
Main_matlab_codes/find_correlation_matrices.m, MATLAB, 89 lines
Main_matlab_codes/fit_wsbm.m, MATLAB, 99 lines, 1 match
Main_matlab_codes/fit_wsbm_mode.m, MATLAB, 190 lines, 1 match
Main_matlab_codes/patterns_generate.m, MATLAB, 167 lines
Main_matlab_codes/patterns_identify.m, MATLAB, 162 lines
Python_codes/draw_c_elegans_network.ipynb, Jupyter, 180 lines
Python_codes/draw_patterns.ipynb, Jupyter, 183 lines
Python_codes/draw_subnetwork_from_matlab_data.ipynb, Jupyter, 384 lines, 1 match
Supporting_matlab_functions/Adj2Edg.m, MATLAB, 47 lines
Supporting_matlab_functions/Conductance_ODE.m, MATLAB, 43 lines, 1 match
Supporting_matlab_functions/Edg2Adj.m, MATLAB, 58 lines
Supporting_matlab_functions/Find_Hopf_Bifurcation_Stepper.m, MATLAB, 59 lines
Supporting_matlab_functions/Run_Model.m, MATLAB, 92 lines, 1 match
Supporting_matlab_functions/Threshold_Voltage.m, MATLAB, 48 lines
Supporting_matlab_functions/biwsbm.m, MATLAB, 164 lines
Supporting_matlab_functions/calc_logEvidence.m, MATLAB, 161 lines
Supporting_matlab_functions/fcn_comm_motifs.m, MATLAB, 114 lines
Supporting_matlab_functions/genlouvain.m, MATLAB, 378 lines
Supporting_matlab_functions/iterated_genlouvain.m, MATLAB, 233 lines
Supporting_matlab_functions/setup_distr.m, MATLAB, 612 lines
Supporting_matlab_functions/wsbm.m, MATLAB, 142 lines, 2 matches
README.md, Text, 53 lines

At the source: github.com/gourab-sar/C-elegans-synchronization

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

Tracing map

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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;
23 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);
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Data

No dataset and no data link were found in the paper.

Data Availability

The open C. elegans data we used in this study were not collected by us and are described in https://doi.org/10.1098/rstb.1986.0056, https://doi.org/10.1371/journal.pcbi.1001066 and can be obtained from WormAtlas https://www.wormatlas.org/. The computational model used in this study is described in https://doi.org/10.1103/PhysRevE.89.052805. Our own code is available here: https://github.com/gourab-sar/C-elegans-synchronization.

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, issue, pages, dates, 4 authors, 11 MeSH terms, 4 funders, 81 references.

Cite

This paper

Sar, G. K., Patton, A., Towlson, E., & Davidsen, J. (2026). Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity. PLoS computational biology, 22(9), e1014152. https://doi.org/10.1371/journal.pcbi.1014152

BibTeX

@article{sar2026synchronization, author = {Sar, Gourab Kumar and Patton, Andrew and Towlson, Emma and Davidsen, Jörn}, title = {{Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity}}, journal = {PLoS computational biology}, year = {2026}, month = sep, volume = {22}, number = {9}, pages = {e1014152}, publisher = {PLOS}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1014152}, url = {https://doi.org/10.1371/journal.pcbi.1014152}, pmid = {42743376}, pmcid = {PMC13588538} }

RIS

TY - JOUR AU - Sar, Gourab Kumar AU - Patton, Andrew AU - Towlson, Emma AU - Davidsen, Jörn TI - Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity T2 - PLoS computational biology J2 - PLoS Comput Biol PY - 2026 DA - 2026/09/15 VL - 22 IS - 9 SP - e1014152 SN - 1553-734X PB - PLOS DO - 10.1371/journal.pcbi.1014152 UR - https://doi.org/10.1371/journal.pcbi.1014152 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pcbi.1014152", "type": "article-journal", "title": "Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity", "container-title": "PLoS computational biology", "author": [ { "family": "Sar", "given": "Gourab Kumar" }, { "family": "Patton", "given": "Andrew" }, { "family": "Towlson", "given": "Emma" }, { "family": "Davidsen", "given": "Jörn" } ], "container-title-short": "PLoS Comput Biol", "volume": "22", "issue": "9", "page": "e1014152", "DOI": "10.1371/journal.pcbi.1014152", "PMID": "42743376", "PMCID": "PMC13588538", "ISSN": "1553-734X", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pcbi.1014152", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 15 ] ] } }

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