OSCR

Burst firing creates an attractor in synaptic weight dynamics.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Julia · 130 lines · 4.2 KB · no license

  1. # Functions
  2. function ISIfunc(Spkt::Array{Float64}) # Computes the interspike intervals out of spike times
  3. ISI = zeros(200000)
  4. l::Int64 = 1
  5. for i = 2:length(Spkt)
  6. ISI[l] = Spkt[i] - Spkt[i-1]
  7. l += 1
  8. end
  9. return ISI
  10. end
  12. function remove0(ISI::Array{Float64}) # Removes 0's in spike times/interspike intervals vectors
  13. f = findall(ISI .== 0)
  14. if f[1] > 1
  15. ISI = ISI[1:f[1]-1]
  16. else
  17. ISI = [0.]
  18. end
  19. return ISI
  20. end
  22. function isbursting(ISI::Array{Float64}) # Check if the neuron is bursting using the rule 3*minISI < maxISI
  23. bursting::Int64 = 0
  24. if minimum(ISI)*4 < maximum(ISI)
  25. bursting = 1
  26. end
  27. return bursting
  28. end
  30. function SPB_PER_DC_IBFfunc(ISI::Array{Float64}) # Computes spike per burst (SPB), period (PER), duty cycle (DC) and mean intraburst frequency of bursting neurons.
  31. minISI = minimum(ISI)
  32. maxISI = maximum(ISI)
  33. interburst = findall(ISI .>= maxISI/3)
  34. intraburst = findall(ISI .<= maxISI/3)
  35. SPB = round(length(intraburst)/length(interburst))+1
  36. IBP = mean(ISI[intraburst])
  37. Burstdur = IBP*(SPB-1)
  38. PER = Burstdur+mean(ISI[interburst])
  39. DC = Burstdur/PER
  40. IBF = 1000/IBP
  41. return (SPB, PER, DC, IBF)
  42. end
  44. function compute_params(spk_ncells::Array{Float64},Ttransient::Int64,tstepstart::Int64,tstepinit::Int64,tstepstop::Int64) # Extracts frequency or (SPB, PER, DC and IBF) out of the spiketimes saved in the Spkt$n.dat files. The number of cells is given by the number of lines in gs.
  45. ncells = length(spk_ncells[:,1])
  47. ISIs_depol = zeros(200000)
  48. ISIs_hyperpol = zeros(200000)
  49. PARAMS_depol = zeros(ncells,4) #[SPB;PER;DC;IBF]
  50. PARAMS_hyperpol = zeros(ncells,4) #[SPB;PER;DC;IBF]
  51. freq_depol = zeros(ncells)
  52. freq_hyperpol = zeros(ncells)
  54. for n = 1:ncells
  55. f1 = findall(spk_ncells[n,:] .>= Ttransient) #Removes the transient of depol period
  56. f2 = findall(spk_ncells[n,:] .>= tstepstart) #Finds end of depol period
  57. f3 = findall(spk_ncells[n,:] .>= tstepinit) #Removes the transient of hyperpol period
  58. f4 = findall(spk_ncells[n,:] .>= tstepstop) #Finds end of hyperpol period
  59. f5 = findall(spk_ncells[n,:] .== 0) #Removes the zeros at the end of the vector
  61. if length(f1) == 0
  62. Spkt_depol = [0.]
  63. Spkt_hyperpol = [0.]
  64. elseif length(f1) == length(f2)
  65. Spkt_depol = [0.]
  66. if length(f3) == length(f4)
  67. Spkt_hyperpol = [0.]
  68. elseif length(f4) > 0
  69. if(isempty(f3))
  70. Spkt_hyperpol = [0.]
  71. else
  72. Spkt_hyperpol = spk_ncells[n,f3[1]:f4[1]-1]
  73. end
  74. else
  75. if(isempty(f5)||isempty(f3))
  76. Spkt_hyperpol = [0.]
  77. else
  78. Spkt_hyperpol = spk_ncells[n,f3[1]:f5[1]-1]
  79. end
  80. end
  81. else
  82. if(isempty(f2))
  83. Spkt_depol = [0.]
  84. else
  85. Spkt_depol = spk_ncells[n,f1[1]:f2[1]-1]
  86. end
  87. if length(f3) == length(f4)
  88. Spkt_hyperpol = [0.]
  89. elseif length(f4) > 0
  90. if(isempty(f3))
  91. Spkt_hyperpol = [0.]
  92. else
  93. Spkt_hyperpol = spk_ncells[n,f3[1]:f4[1]-1]
  94. end
  95. else
  96. if(isempty(f5)||isempty(f3))
  97. Spkt_hyperpol = [0.]
  98. else
  99. Spkt_hyperpol = spk_ncells[n,f3[1]:f5[1]-1]
  100. end
  101. end
  102. end
  104. ISI_depol = ISIfunc(Spkt_depol)
  105. ISI_hyperpol = ISIfunc(Spkt_hyperpol)
  106. ISItemp_depol::Array{Float64} = remove0(ISI_depol)
  107. ISItemp_hyperpol::Array{Float64} = remove0(ISI_hyperpol)
  108. if isbursting(ISItemp_depol) == 1
  109. (PARAMS_depol[n,1],PARAMS_depol[n,2],PARAMS_depol[n,3],PARAMS_depol[n,4]) = SPB_PER_DC_IBFfunc(ISItemp_depol)
  110. #freq_depol[n] = 0.
  111. else
  112. PARAMS_depol[n,:] .= 0.
  113. freq_depol[n] = 1000/mean(ISItemp_depol)
  114. end
  116. if isbursting(ISItemp_hyperpol) == 1
  117. (PARAMS_hyperpol[n,1],PARAMS_hyperpol[n,2],PARAMS_hyperpol[n,3],PARAMS_hyperpol[n,4]) = SPB_PER_DC_IBFfunc(ISItemp_hyperpol)
  118. #freq_hyperpol[n] = 0.
  119. else
  120. PARAMS_hyperpol[n,:] .= 0.
  121. freq_hyperpol[n] = 1000/mean(ISItemp_hyperpol)
  122. end
  123. ISIs_depol = [ISIs_depol ISI_depol]
  124. ISIs_hyperpol = [ISIs_hyperpol ISI_hyperpol]
  125. end
  126. ISIs_depol = ISIs_depol[:,2:end]
  127. ISIs_hyperpol = ISIs_hyperpol[:,2:end]
  129. return ISIs_depol, ISIs_hyperpol, PARAMS_depol, PARAMS_hyperpol, freq_depol, freq_hyperpol
  130. end

PARAMS_cycle.jl at commit f3d3133, no license · at the source

Overview

Authors: Kathleen Jacquerie1,2, Danil Tyulmankov3,4, Pierre Sacré2, Guillaume Drion2
ORCID iDs: Kathleen Jacquerie
  1. Biology Department, Brandeis University, Waltham, Massachusetts, United States of America
  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
  3. Viterbi School of Engineering, University of Southern California, Los Angeles, California, United States of America
  4. Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
Institutions: University of Liège (Belgium); Brandeis University (United States); University of Southern California (United States); Columbia University (United States)
Journal: PLoS computational biology, volume 22, issue 3, article e1014001
Dates: received 23 September 2025; accepted 9 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014001 · PMID 41801985 · PMCID PMC13108889 · OpenAlex W7134233991
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism)
Methods: Spectral & time-frequency
MeSH: Action Potentials*, Models, Neurological*, Nerve Net*, Neuronal Plasticity*, Synapses*, Animals, Computational Biology, Computer Simulation, Neurons (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fonds De La Recherche Scientifique - FNRS (ASP40006590); Belgian Federal Science Policy Office (NEMODEI2)
Citations: cited by 1 paper (Europe PMC); 72 references in the paper

Abstract

Neural circuits often alternate between tonic and burst firing, two distinct activity regimes that reflect changes in excitability and neuromodulatory state. While tonic firing produces asynchronous spikes driven by diverse external inputs, collective burst firing consists of rapid clusters of spikes followed by a period of silence, happening synchronously within the network. Synaptic plasticity has typically been studied only in either one of these regimes, leaving unclear how their distinct plasticity dynamics can be combined when circuits alternate between regimes. Here, we use a conductance-based network model endowed with calcium-based or spike-timing–based plasticity rules to examine how synaptic weights evolve across tonic and burst firing regimes. During tonic firing, synaptic weights are driven by the statistics of external inputs, producing a broad distribution across the network. In contrast, during collective burst firing, weights converge to a narrow region in weight space: a burst-induced attractor. We derive the location of this attractor analytically in terms of plasticity parameters and activity statistics, and confirm its emergence across diverse plasticity rules. The attractor reflects the synchronization of plasticity-driving signals during bursts, which homogenizes synaptic dynamics and forces convergence toward shared fixed points. We further show that neuromodulation and synaptic tagging can shift or split the burst-induced attractor, stabilizing selected synapses while weakening others. Together, these results identify burst-induced attractors as a robust emergent property of collective bursting. Alternation between tonic and burst firing provides a biologically plausible context in which heterogeneous, input-driven synaptic configurations formed during tonic activity can be selectively consolidated or down-selected by the burst-induced attractor during subsequent bursts. By showing how they can be analytically predicted and experimentally modulated, our work provides a general computational framework linking firing state transitions, synaptic plasticity, and memory organization.

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

Repository

Its files are read in the Code ↔ Paper reader above.

KJacquerie/Burst-Attractor

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f3d31332899dc4a53ed7d4e623443d653430d3fa, 14 February 2026
Languages: Julia (33), MATLAB (8)
Size: 115 files, 41 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DataFrames.jl (14 files), DifferentialEquations.jl (14 files), Plots.jl (14 files), Distributions.jl (13 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
42 files
At the source: github.com/KJacquerie/Burst-Attractor

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;
  • 41 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 Availability

The code files are freely available at https://github.com/KJacquerie/Burst-Attractor.

Reproduced under the paper's license (CC BY), 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 MeSH terms, 2 funders, 72 references.

Cite

This paper

Jacquerie, K., Tyulmankov, D., Sacré, P., & Drion, G. (2026). Burst firing creates an attractor in synaptic weight dynamics. PLoS computational biology, 22(3), e1014001. https://doi.org/10.1371/journal.pcbi.1014001

BibTeX

@article{jacquerie2026burst,
author = {Jacquerie, Kathleen and Tyulmankov, Danil and Sacré, Pierre and Drion, Guillaume},
title = {{Burst firing creates an attractor in synaptic weight dynamics}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1014001},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014001},
url = {https://doi.org/10.1371/journal.pcbi.1014001},
pmid = {41801985},
pmcid = {PMC13108889}
}

RIS

TY - JOUR
AU - Jacquerie, Kathleen
AU - Tyulmankov, Danil
AU - Sacré, Pierre
AU - Drion, Guillaume
TI - Burst firing creates an attractor in synaptic weight dynamics
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/09
VL - 22
IS - 3
SP - e1014001
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014001
UR - https://doi.org/10.1371/journal.pcbi.1014001
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1014001",
"type": "article-journal",
"title": "Burst firing creates an attractor in synaptic weight dynamics",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Jacquerie",
"given": "Kathleen"
},
{
"family": "Tyulmankov",
"given": "Danil"
},
{
"family": "Sacré",
"given": "Pierre"
},
{
"family": "Drion",
"given": "Guillaume"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "3",
"page": "e1014001",
"DOI": "10.1371/journal.pcbi.1014001",
"PMID": "41801985",
"PMCID": "PMC13108889",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014001",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
9
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/pnasnexus/pgag213 [code]
Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing.
Journal: PNAS nexus
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 1 other tool, 31 references, author Kathleen Jacquerie
[2] doi:10.1371/journal.pcbi.1014730 [code]
A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity.
Journal: PLoS computational biology
In common: computational modeling (no new data), 8 references
[3] doi:10.1038/s41540-026-00749-5 [code]
A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations.
Journal: NPJ systems biology and applications
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 1 other tool, computational modeling (no new data), 1 reference
[4] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 2 other tools
[5] doi:10.1371/journal.pcbi.1014177 [code]
Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function.
Journal: PLoS computational biology
In common: DifferentialEquations.jl, Plots.jl, none (in silico), computational modeling (no new data), 3 references
[6] doi:10.1371/journal.pcbi.1014575 [code]
Remembering the "when": Hebbian memory models for the time of past events.
Journal: PLoS computational biology
In common: DataFrames.jl, Distributions.jl, computational modeling (no new data), 3 references
[7] doi:10.1523/eneuro.0379-25.2026 [code]
Next-Generation Neural Mass Models Reproduce Features of Speech Processing.
Journal: eNeuro
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 1 other tool
[8] doi:10.1371/journal.pcbi.1014222 [code]
Neural population models for EEG: From Canonical models to alternative model structures.
Journal: PLoS computational biology
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 1 other tool
[9] doi:10.1371/journal.pcbi.1014549 [code]
Inward rectifier potassium channels interact with calcium channels to promote robust and physiological bistability.
Journal: PLoS computational biology
In common: DifferentialEquations.jl, DataFrames.jl, Plots.jl, 1 other tool
[10] doi:10.1162/imag.a.1249 [code]
Global search metaheuristics for neural mass model calibration.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: DifferentialEquations.jl, Plots.jl, Distributions.jl, 1 other tool, none (in silico)

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.