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Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity.

Code ↔ Paper

8 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 8 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Network model ↔ SpikesToFluoresence.m, the whole file · a weak match · score 0.98 · double exponential kernel, synthetic fluorescence signal, latent variable, external noise, phenomenological model, sigmoidal function
  2. [2] § STAR★Methods › Method details › Network model ↔ Gillespie_EImodel.m, the whole file · a weak match · score 0.87 · stochastic Wilson Cowan, quiescent state, transition rate, connectivity matrix, vectors, dimensional
  3. [3] § Results › E-I network model suggests that neuronal avalanches emerge through balanced amplification ↔ run_EImodel.m, lines 1–39 · score 0.84 · nonlinear response function, model spiking activity, neurons interact, exponentially decreased, transients, distance
  4. [4] § STAR★Methods › Method details › Neuronal avalanches ↔ Get_NonSpatialAvalanches.m, the whole file · a weak match · score 0.71 · summed population activity, recorded neurons, definition, threshold, bins, Neuronal avalanche
  5. [5] § Results › E-I network model suggests that neuronal avalanches emerge through balanced amplification ↔ Get_NonSpatialAvalanches.m, the whole file · a weak match · score 0.64 · spatially unconstrained definition, sum activity, population activities, neuronal avalanches, neurons
  6. [6] § STAR★Methods › Method details › Network model ↔ Gillespie_EImodel.m, the whole file · a weak match · score 0.63 · transition rate, Gillespie, quiescent, algorithm, dt, simulated
  7. [7] § STAR★Methods › Method details › Network model ↔ Get_Connectivity_matrix.m, the whole file · a weak match · score 0.58 · spatial embedded, connectivity matrix, NI, coupling, neurons
  8. [8] § STAR★Methods › Method details › Network model ↔ Get_Connectivity_matrix.m, the whole file · a weak match · score 0.50 · balanced amplification, Schur, rI, trace, wI, coupling

Paper

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

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The authors' code

MATLAB · 137 lines · 4.5 KB · no license · 2 matches

  1. function [spike_times,spike_ids,network_state] = Gillespie_EImodel(W,response_fn,beta,alpha,I,t_min,t_max,init_state)
  3. % This function is based on Edward Wallace's code, publicly available here:
  4. % https://github.com/ewallace/stochsimcode
  5. %
  6. % This code simulates a stochastic Wilson-Cowan model with the Gillespie algorithm.
  7. % The connectivity of E and I neurons is given by matrix W (N-by-N, where N = NE + NI).
  8. % The background inputs are given by I (N-dimensional).
  9. % State transition rates are given by alpha and the tranfert function (response_fn, handle function)
  10. %
  11. %
  12. % Inputs:
  13. % -W: connectivity matrix, N-by-N: W(i,j) is synaptic
  14. % weight from the jth neuron to the ith neuron.
  15. % -response_fn: handle for response function (sigmoid, hyperbolic tan, etc.)
  16. % -I: backgound inputs, N-dimensional array
  17. % -alpha: rate at which active neurons decay to quiescent state, N-dimensional array
  18. % -beta: is the height of the response function, N-dimensional array
  19. % -init_state: is the initial state vector, 2-by-N
  20. %
  21. % Outputs:
  22. % - spike_times: spike times
  23. % - spike_ids: neuron IDs associated to spike times
  24. % - network_state: last network state (useful for batches)
  25. %
  26. % References:
  27. % Citation: Benayoun M, Cowan JD, van Drongelen W, Wallace E (2010)
  28. % Avalanches in a Stochastic Model of Spiking Neurons.
  29. % PLoS Comput Biol 6(7): e1000846.
  30. % https://doi.org/10.1371/journal.pcbi.1000846
  31. %
  32. % Wallace E, Benayoun M, van Drongelen W, Cowan JD.
  33. % Emergent Oscillations in Networks of Stochastic Spiking Neurons
  34. % PLoS ONE, May 6, 2011. DOI: 10.1371/journal.pone.0014804
  35. %--------------------------------------------------------------------------
  37. N = size(W,1);
  39. % Calculate expected number of events
  40. factor=10;
  41. expected_events=N*(t_max-t_min)*factor;
  43. % Initialize vectors for update times, label of updated neuron,
  44. % and new state of updated neuron
  45. times = zeros(1, expected_events);
  46. updates = times;
  47. new_states = zeros(2, expected_events);
  49. % Set event counter to 0 and simulation time to initial time.
  50. event_no = 0;
  51. curr_time = t_min;
  52. dt = 0;
  54. % initialize network state vector - we'll keep one vector
  55. % for the active neurons and another for the quiescent ones
  56. network_state = init_state;
  57. active = init_state(1,:)';
  58. quiescent = init_state(2,:)';
  60. % Calculate vector of transition rates at initial time
  61. currents = W*active + I;
  62. trans = beta .* (active==0) .*feval(response_fn,currents) + ...
  63. alpha.*(active==1);
  64. cum_trans=cumsum(trans);
  67. % Main loop: update according to Gillespie algorithm, with rates
  68. % specified by trans, until time t_max is exceeded.
  70. while (curr_time < t_max)
  71. curr_time = curr_time + dt;
  73. % Call gillespie to pick update time, neuron updated, and new state
  75. % Calculates total network transition rate, as sum of transition
  76. % rates of all neurons, i.e. last element of cum_trans.
  77. total_trans = cum_trans(end);
  79. % timestep is exponential R.V. with parameter total_trans
  80. dt = -log(rand)/total_trans;
  82. % pick random variable uniform on (0, total_trans), and select
  83. % neuron with number i_update as least i with
  84. % test_variable < cum_trans(i)
  85. test_variable = total_trans*rand;
  87. i_update = find(cum_trans >= test_variable,1,'first');
  89. % Pick new state for neuron i_update
  90. new_state = double(~network_state(:,i_update));
  92. active(i_update) = new_state(1);
  93. quiescent(i_update) = new_state(2);
  95. % change transition rates of neurons affected by spike
  96. if(new_state(1) == 1)
  97. currents = currents + W(:,i_update);
  98. elseif(new_state(1)==0)
  99. currents = currents - W(:,i_update);
  100. end
  101. trans = beta .* (active==0) .*feval(response_fn,currents) + ...
  102. alpha.*(active==1);
  105. % calculate cumulative sum of transition probabities
  106. cum_trans=cumsum(trans);
  109. event_no=event_no+1;
  110. times(event_no)=curr_time+dt;
  111. updates(event_no) = i_update;
  112. new_states(:, event_no)=new_state;
  113. network_state = [ active' ; quiescent' ];
  115. % If network can make no further transitions, end simulation.
  116. if(cum_trans(N)==0)
  117. break
  118. end
  119. end
  120. % End of main simulation loop
  122. event_no = event_no - 1;
  123. times=times(1:event_no);
  124. updates=updates(1:event_no);
  125. new_states=new_states(:, 1:event_no);
  128. % Takes vectors of all times, updates, and new states, outputs
  129. % reduced vector containing only spike times and info, i.e.
  130. % transitions from quiescent to active.
  131. spikes = find(new_states(1,:));
  133. spike_times = times(spikes);
  134. spike_ids = updates(spikes);
  137. return

Gillespie_EImodel.m at commit cbfcd64, no license · at the source

Overview

Authors: Maxime Janbon1, Mateo Amortegui1, Enrique Carlos Arnoldo Hansen1, Sarah Nourin1, Virginie Candat1, Germán Sumbre1, Adrián Ponce-Alvarez2,3,4
ORCID iDs: Enrique Carlos Arnoldo Hansen, Virginie Candat, Adrián Ponce-Alvarez
  1. Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
  2. Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
  3. Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain
  4. Centre de Recerca Matemàtica, Barcelona, Spain
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Université Paris Sciences et Lettres (France); École Normale Supérieure - PSL (France); Institut de Biologie de l'École Normale Supérieure (France); Centre de Recerca Matemàtica (Spain); Universitat Politècnica de Catalunya (Spain)
Journal: iScience, volume 29, issue 9, article 117212
Dates: received 5 November 2025; accepted 30 July 2026; published online 14 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.117212 · PMID 42633153 · PMCID PMC13499105 · OpenAlex W7203452248
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), zebrafish (organism)
Methods: Connectivity, Statistics, Single-unit activity, calcium imaging, Spectral & time-frequency
Keywords: zebrafish, optic tectum, calcium imaging, light-sheet microscopy, immunostaining, neuronal avalanches, excitation-inhibition balance, stochastic network model, critical neuronal dynamics
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: State Agency of Research (PID2022-137708NB-I00, RYC2020-029117-I, CEX2020-001084-M); European Research Council (726280); Federación Española de Enfermedades Raras
Citations: cited by 1 paper (Europe PMC); 88 references in the paper

Abstract

Neuronal avalanches are sequences of neural activations exhibiting scale-invariant statistics, indicative of critical dynamics. Theoretical studies proposed that the balance between excitation (E) and inhibition (I), along with neuromodulation, are key factors influencing this critical behavior. Here, we performed in vivo studies to investigate the role of E and I neurons in generating neuronal avalanches in the optic tectum of zebrafish larvae. For this, we used double-transgenic zebrafish larvae expressing cell-type-specific fluorescent proteins and GCaMP6f, combined with immunostaining and selective-plane illumination microscopy to monitor spontaneous neuronal activity and neurotransmitter identity. We found that neural activity exhibited avalanches with critical exponents at balanced and slightly excitation-dominated E–I ratios, whereas imbalanced ratios led to faster-decaying avalanches. A stochastic network model operating at a critical point, where excitation and inhibition couplings are balanced and balanced amplification drives network avalanches, reproduced the observed statistics of neuronal avalanches and their dependence on E-I ratio fluctuations.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

adrianponce/ExcInh_Neuronal_Avalanches

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cbfcd64521944fa5b13910ae3ca9bcdf708de2cd, 21 October 2025
Languages: MATLAB (7)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/adrianponce/ExcInh_Neuronal_Avalanches

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;
  • 7 scripts, each with its path and the digest of its content;
  • 8 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

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

Data and code availability

All data reported in this article is available upon reasonable request.

The codes to detect and analyze the neuronal avalanches and to model them are available at: https://github.com/adrianponce/ExcInh_Neuronal_Avalanches.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Adrián Ponce-Alvarez (0000-0003-1446-7392); removed Adrián Ponce-Alvarez

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 keywords, 3 funders, 88 references.

Cite

This paper

Janbon, M., Amortegui, M., Hansen, E. C. A., Nourin, S., Candat, V., Sumbre, G., & Ponce-Alvarez, A. (2026). Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity. iScience, 29(9), 117212. https://doi.org/10.1016/j.isci.2026.117212

BibTeX

@article{janbon2026critical,
author = {Janbon, Maxime and Amortegui, Mateo and Hansen, Enrique Carlos Arnoldo and Nourin, Sarah and Candat, Virginie and Sumbre, Germán and Ponce-Alvarez, Adrián},
title = {{Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117212},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117212},
url = {https://doi.org/10.1016/j.isci.2026.117212},
pmid = {42633153},
pmcid = {PMC13499105}
}

RIS

TY - JOUR
AU - Janbon, Maxime
AU - Amortegui, Mateo
AU - Hansen, Enrique Carlos Arnoldo
AU - Nourin, Sarah
AU - Candat, Virginie
AU - Sumbre, Germán
AU - Ponce-Alvarez, Adrián
TI - Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/14
VL - 29
IS - 9
SP - 117212
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117212
UR - https://doi.org/10.1016/j.isci.2026.117212
LA - en
ER -

CSL-JSON

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