Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing.

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[1] § Results › Coupling gain and initial state affect consolidation dynamics during burst firing ↔ Fig4/julia/Simu_SNR_GB2012_VAR.jl, lines 145–183 · score 0.54 · 0.1–5 Hz, 73–76 Hz, 73 Hz, 0.1 Hz, neuron, burst

Paper

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The authors' code

Julia · 243 lines · 6.7 KB · no license · 1 match

# Defines output directory
directory_name = "/Users/kathleen/Documents/PhD/2023-Project/Fig5"
# Loads packages
using Plots
using DelimitedFiles
using Statistics
using Images, ImageView
using DifferentialEquations
using DataFrames
using Printf
using CSV
using LinearAlgebra
using Distributions
using DataStructures
using DSP
# STRUCTURAL plasticity
#const tauG = 400 # (strong) or 800 (weak)
#gCAMPA = 0.001*ones(nPre,nPost)
## Include model
include("model_SNR_GB2012_VAR.jl")
include("PARAMS_cycle.jl")
const new_network=0
const fMax = 50
const bound_type = "SB"
const experiment_type = "normal" #high-dep
experiment_name = "Graupner2012_VAR"
# Network parameters
const ncellsI = 1
const nPre = 100
const nPost = 1
const ncellsC = nPre+nPost
const ncells = ncellsI+ncellsC
## Simulation parameters
const dt = 0.01
const N_cycles=5
const Duration_cycle = 30000
const Tdt_cycle = convert(Int64, Duration_cycle/dt)
const N_patterns = 1
const Duration_state = convert(Int64, Duration_cycle/2)
const Tdt_state = convert(Int64, Duration_state/dt)
const N_samples = 1
const Duration_sample = convert(Int64, Duration_state/N_samples)
const Tdt_sample = convert(Int64, Duration_sample / dt)
const T = N_cycles*Duration_cycle
const Tdt = convert(Int64, T / dt)
const t = range(dt, T, length = Tdt)
const Duration_set = Duration_cycle
const Tdt_set = convert(Int64, Duration_set/dt)
const state = zeros(Tdt,1)
for idx_cycle=1:1:N_cycles
# wake
state[(idx_cycle-1)*Tdt_set+1: (idx_cycle-1)*Tdt_set+Tdt_state] .= 0
# sleep
#if(SD=="yes")
# state[(idx_cycle-1)*Tdt_set+Tdt_state+1:(idx_cycle-1)*Tdt_set+Tdt_cycle ] .= -1
#else
state[(idx_cycle-1)*Tdt_set+Tdt_state+1:(idx_cycle-1)*Tdt_set+Tdt_cycle ] .= 1
#end
end
## Neurons' model parameters
# Global parameters
const C = 1
const VNa = 50
const VK = -85
const VCa = 120
const Vl = -55
const VH = -20
const Kd = 170
# Cells parameters
const gl = 0.055
const gNa = 170.0
const gKd = 40
const k1 = 1.e-1
const k2 = 0.1e-1
const gH = 0.01
const gKCa = 4
const gCaT = 0.55
if(new_network==1 && fMax==50)
const gamma = 0.10 #1 #10% of variability in the network put 0.1 to get 10% etc
const gl_cells = rand(Uniform(gl*(1-gamma),gl*(1+gamma)),ncells)
const gNa_cells = rand(Uniform(gNa*(1-gamma),gNa*(1+gamma)),ncells)
const gKd_cells = rand(Uniform(gKd*(1-gamma),gKd*(1+gamma)),ncells)
const k1_cells = rand(Uniform(k1*(1-gamma),k1*(1+gamma)),ncells) #k1*ones(ncells)
const k2_cells = rand(Uniform(k2*(1-gamma),k2*(1+gamma)),ncells) #k2*ones(ncells)
const gH_cells = rand(Uniform(gH*(1-gamma),gH+(gamma*gH)),ncells)
const gKCa_cells = rand(Uniform(gKCa*(1-gamma),gKCa*(1+gamma)),ncells)
const gCaT_cells = rand(Uniform(gCaT*(1-gamma),gCaT*(1+gamma)),ncells)
const g_cond = [gNa_cells'; gKd_cells'; gCaT_cells'; gH_cells'; gKCa_cells'; gl_cells'; k1_cells'; k2_cells']'
writedlm(@sprintf("%s/data/%s/gion.dat",directory_name, experiment_name), g_cond, header=false)
else
gion = readdlm(@sprintf("%s/data/%s/gion.dat",directory_name, experiment_name))
const gl_cells = gion[:,6]
const gNa_cells = gion[:,1]
const gKd_cells = gion[:,2]
const k1_cells = gion[:,end-1]
const k2_cells = gion[:,end]
const gH_cells = gion[:,4]
const gKCa_cells = gion[:,5]
const gCaT_cells = gion[:,3]
end
const IappI = 3.
const IappC = 0.
const spike_duration = 3
const IstepI = -1.2-IappI
const IstepC = 50.0
IstepI_cell = IstepI .* ones(ncellsI)
IstepC_cell = IstepC .* ones(ncellsC)
Istep_cell = [IstepI_cell; IstepC_cell]
const N_states = N_cycles*2
if(new_network==1)
gamma=0.1
neurons_freq = zeros(N_samples*N_states, ncells)
neurons_freq[:,1] .= 1 # inhibitory cell
neurons_freq[:,2:6] = round.(rand(Uniform(50,60),N_samples*N_states,5))
neurons_freq[:,7:end-1] = round.(rand(Uniform(0.1,1),N_samples*N_states,95))
neurons_freq[:,end] .= 25.00
#=
neurons_freq[:,2:6] = round.(rand(Uniform(73,76),N_samples*N_states,5))
neurons_freq[:,7:end-1] = round.(rand(Uniform(0.1,5),N_samples*N_states,95))
neurons_freq[:,end] .= 25.00
=#
writedlm(@sprintf("%s/data/%s/neurons_freq.dat", directory_name, experiment_name), neurons_freq, header=false)
else
neurons_freq= readdlm(@sprintf("%s/data/%s/neurons_freq.dat",directory_name, experiment_name))
end
Iapp_cell = zeros(ncells, Tdt)
for idx_cycle= 1:1:N_cycles
for idx=1:1:N_samples
T1 = (idx_cycle-1)*Tdt_set +(idx-1)*Tdt_sample+1
T2 = (idx_cycle-1)*Tdt_set + idx*Tdt_sample
Iapp_cell[:, T1:T2] = get_Iapp(Duration_sample, dt, neurons_freq[(idx_cycle-1)*N_samples+idx,:], spike_duration)
end
end
Iapp_cell[1:ncellsI,:] .= IappI
const BurstTime = zeros(1,N_cycles)
for idx=1:1:N_cycles
BurstTime[idx] = Duration_state + (idx-1)*Duration_set
end
const BurstDuration = Duration_state #20000
const StateTime = zeros(1,N_cycles*2)
let idx_count
idx_count=1
for idx_cycle=1:1:N_cycles
StateTime[idx_count] = (idx_cycle-1)*Duration_set+Duration_state
StateTime[idx_count+1] = (idx_cycle-1)*Duration_set+Duration_cycle
idx_count = idx_count+2
end
end
## synaptic plasticity
const expm = "Control"
# SJO param
const tau_Ca = 22.6936 #[ms]
const C_Pre = 0.56#17539
const C_Post = 1.24#23964
const D_pre = 4.60#98 #[ms]
const tau_w = 346.3615e3 #[s>ms]
const gamma_p = 725.085*1.1#*0.7
const gamma_p_sleep = 725.085*0.95
const gamma_d = 331.909#*0.7
const theta_p = 1.3
const theta_d = 1.
const wfix = 0.5
## CONNECTIVITY
const gIGABAA_unit = 2.0
const gIGABAB_unit = 1.5
if(new_network==1 && fMax==50)
const gIGABAA = rand(Uniform(gIGABAA_unit*(1-gamma),gIGABAA_unit*(1+gamma)),ncellsC)./ ncellsI
const gIGABAB = rand(Uniform(gIGABAB_unit*(1-gamma),gIGABAB_unit*(1+gamma)),ncellsC)./ ncellsI
const g_syn = [gIGABAA'; gIGABAB']'
writedlm(@sprintf("%s/data/%s/gsyn.dat",directory_name, experiment_name), g_syn, header=false)
else
gsyn = readdlm(@sprintf("%s/data/%s/gsyn.dat",directory_name, experiment_name))
const gIGABAA = gsyn[:,1]
const gIGABAB = gsyn[:,2]
end
w_init = 0.5*ones(nPre,nPost)
#idx_wl = convert(Matrix{Int64}, readdlm("idx_wl.dat"))
gCAMPA_init_mat = [0.0001 0.00025 0.0005 0.00075 0.001 0.0025 0.005 0.0075 0.01]
tauG_mat = [10 20 30 40 50 60 70 80 90 100 200 300 400 500 600 700 800 900 1000 2000 ]
for idx_tauG=1:1:length(tauG_mat)
for idx_gCAMPA_init=1:1:length(gCAMPA_init_mat)
println("tauG=", tauG_mat[idx_tauG])
println("gCAMPA0=", gCAMPA_init_mat[idx_gCAMPA_init])
@time () = simulateTOY_ncellsScenarioNMOD(
ncells,
ncellsI,
ncellsC,
Iapp_cell,
Istep_cell,
gCAMPA_init_mat[idx_gCAMPA_init],
tauG_mat[idx_tauG],
idx_gCAMPA_init
)
end
end

Simu_SNR_GB2012_VAR.jl at commit 30b9289, no license · at the source

Overview

Authors: Kathleen Jacquerie^1,2, Danil Tyulmankov^3,4, Pierre Sacré², Guillaume Drion²

ORCID iDs: Kathleen Jacquerie, Danil Tyulmankov, Pierre Sacré, Guillaume Drion

Institutions: University of Liège (Belgium); Brandeis University (United States); University of Southern California (United States); Columbia University (United States)

Journal: PNAS nexus, volume 5, issue 7, article pgag213

Dates: received 29 November 2025; accepted 1 June 2026; published online 12 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1093/pnasnexus/pgag213 · PMID 42394754 · PMCID PMC13323795 · OpenAlex W7164503266

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: cellular / molecular (subfield)

Methods: Spectral & time-frequency

Keywords: neuromodulation, eligibility trace, structural synaptic plasticity, neuromodulated synaptic plasticity

Journal subjects: Physical Sciences and Engineering, Biophysics and Computational Biology

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Fonds De La Recherche Scientifique - FNRS (ASP40006590); Belgian Government

Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

How can brain circuits remain plastic enough to encode new information while still stabilizing synaptic changes that support long-term memory? Many circuits switch between tonic spiking, which encodes external inputs, and burst firing, which is generated collectively; yet how these firing-state changes interact with synaptic plasticity to support consolidation remains unclear. Here, we ask whether burst epochs can provide a minimal, mechanistically interpretable route to stabilizing memories encoded during tonic firing. We introduce a two-factor synaptic plasticity rule in a conductance-based spiking network that switches robustly between tonic and burst regimes. The effective synaptic strength is expressed as the product of two factors: a primary, flexible factor updated by a Hebbian mechanism, and a secondary factor that captures stabilizing processes. The secondary factor is adjusted according to the rate of change of the primary factor. In a pattern recognition case study, the network encodes new inputs during tonic firing and undergoes burst epochs. This two-factor rule stabilizes previously learned patterns, integrates information across samples to support generalization, and improves robustness to noise. Ablation experiments show that these outcomes require a synergy between neural bursting activity and the two-factor plasticity rule: blocking secondary plasticity prevents stable retention, replacing bursts with quiescence leads to fading memories, and replacing bursts with additional tonic firing causes interference and noise sensitivity. Finally, a signal-to-noise ratio analysis across tonic-burst cycles identifies parameter regimes in which bursts either sharpen or weaken synaptic representations, consistent with consolidation or pruning, respectively.

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 1 match between paragraphs and lines of code.

KJacquerie/Two-Factor-Plasticity

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 30b928968796ce98b3a27f8f31b99ee4d5d0d039, 7 December 2025

Languages: Julia (33), MATLAB (15)

Size: 1,821 files, 48 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 (16 files), DifferentialEquations.jl (16 files), Distributions.jl (16 files), Plots.jl (16 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

49 files

Fig1B/julia/PARAMS_cycle.jl, Julia, 130 lines
Fig1B/julia/Simu_scenario_GB2012.jl, Julia, 349 lines
Fig1B/julia/model_scenario_GB2012.jl, Julia, 403 lines
Fig1B/matlab/Fig1_plotV.m, MATLAB, 831 lines
Fig1E-H/julia/PARAMS_cycle.jl, Julia, 130 lines
Fig1E-H/julia/Simu_scenario_GB2012.jl, Julia, 363 lines
Fig1E-H/julia/Simu_scenario_GB2012_RESET.jl, Julia, 358 lines
Fig1E-H/julia/Simu_scenario_GB2012_TONIC.jl, Julia, 348 lines
Fig1E-H/julia/Simu_scenario_GB2012_noBURST.jl, Julia, 348 lines
Fig1E-H/julia/Simu_scenario_GB2012_silent.jl, Julia, 347 lines
Fig1E-H/julia/model_scenario_GB2012.jl, Julia, 395 lines
Fig1E-H/julia/model_scenario_GB2012_RESET.jl, Julia, 393 lines
Fig1E-H/julia/model_scenario_GB2012_TONIC.jl, Julia, 392 lines
Fig1E-H/julia/model_scenario_GB2012_noBURST.jl, Julia, 392 lines
Fig1E-H/julia/model_scenario_GB2012_silent.jl, Julia, 392 lines
Fig1E-H/matlab/Fig1_wl.m, MATLAB, 262 lines
Fig2_MNIST_long/julia/Simu_MNIST_GB2012.jl, Julia, 284 lines
Fig2_MNIST_long/julia/Simu_MNIST_GB2012_RESET.jl, Julia, 227 lines
Fig2_MNIST_long/julia/Simu_MNIST_GB2012_learn.jl, Julia, 271 lines
Fig2_MNIST_long/julia/Simu_MNIST_GB2012_noBURST.jl, Julia, 270 lines
Fig2_MNIST_long/julia/model_MNIST_GB2012.jl, Julia, 414 lines
Fig2_MNIST_long/julia/model_MNIST_GB2012_RESET.jl, Julia, 421 lines
Fig2_MNIST_long/julia/model_MNIST_GB2012_learn.jl, Julia, 417 lines
Fig2_MNIST_long/julia/model_MNIST_GB2012_noBURST.jl, Julia, 417 lines
Fig2_MNIST_long/matlab/RF_MNIST.m, MATLAB, 236 lines
Fig2_MNIST_long/matlab/RF_MNIST_corr.m, MATLAB, 381 lines
Fig2_MNIST_long/matlab/hex2rgb.m, MATLAB, 107 lines
Fig2_MNIST_long/matlab/mean_dataset.m, MATLAB, 91 lines
Fig3/julia/Simu_MNIST_GB2012_PREDICTION_ntk_fair.jl, Julia, 250 lines
Fig3/julia/model_MNIST_GB2012_fair.jl, Julia, 295 lines
Fig3/matlab/RF_MNIST_corr.m, MATLAB, 381 lines
Fig3/matlab/plot_prediction_train.m, MATLAB, 131 lines
Fig4/julia/PARAMS_cycle.jl, Julia, 130 lines
Fig4/julia/Simu_SNR_GB2012_VAR.jl, Julia, 243 lines, 1 match
Fig4/julia/model_SNR_GB2012_VAR.jl, Julia, 426 lines
Fig4/matlab/Fig4_var_quantif.m, MATLAB, 212 lines
Fig4/matlab/bgd_SD.m, MATLAB, 24 lines
Fig4/matlab/bgd_seq.m, MATLAB, 24 lines
Fig4/matlab/hex2rgb.m, MATLAB, 107 lines
Fig5/julia/Simu_OL_GB2012.jl, Julia, 360 lines
Fig5/julia/Simu_OL_GB2016.jl, Julia, 350 lines
Fig5/julia/Simu_nOL_GB2012.jl, Julia, 360 lines
Fig5/julia/Simu_nOL_GB2016.jl, Julia, 343 lines
Fig5/julia/model_GB2012.jl, Julia, 387 lines
Fig5/julia/model_GB2016.jl, Julia, 406 lines
Fig5/matlab/Fig5_RF.m, MATLAB, 216 lines
Fig5/matlab/Fig5_quantif.m, MATLAB, 218 lines
Fig5/matlab/hex2rgb.m, MATLAB, 107 lines
README.md, Text, 98 lines

At the source: github.com/KJacquerie/Two-Factor-Plasticity

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

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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;
48 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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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

All original data in this work were generated using the Julia programming language (74). Analyses were performed in Matlab. The code files are freely available at https://github.com/KJacquerie/Two-Factor-Plasticity. Any additional information can be requested from the lead contact ().

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 2 funders, 70 references.

Cite

This paper

Jacquerie, K., Tyulmankov, D., Sacré, P., & Drion, G. (2026). Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing. PNAS nexus, 5(7), pgag213. https://doi.org/10.1093/pnasnexus/pgag213

BibTeX

@article{jacquerie2026two, author = {Jacquerie, Kathleen and Tyulmankov, Danil and Sacré, Pierre and Drion, Guillaume}, title = {{Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing}}, journal = {PNAS nexus}, year = {2026}, month = jun, volume = {5}, number = {7}, pages = {pgag213}, publisher = {Oxford University Press}, issn = {2752-6542}, doi = {10.1093/pnasnexus/pgag213}, url = {https://doi.org/10.1093/pnasnexus/pgag213}, pmid = {42394754}, pmcid = {PMC13323795} }

RIS

TY - JOUR AU - Jacquerie, Kathleen AU - Tyulmankov, Danil AU - Sacré, Pierre AU - Drion, Guillaume TI - Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing T2 - PNAS nexus J2 - PNAS Nexus PY - 2026 DA - 2026/06/12 VL - 5 IS - 7 SP - pgag213 SN - 2752-6542 PB - Oxford University Press DO - 10.1093/pnasnexus/pgag213 UR - https://doi.org/10.1093/pnasnexus/pgag213 LA - en ER -

CSL-JSON

{ "id": "10.1093/pnasnexus/pgag213", "type": "article-journal", "title": "Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing", "container-title": "PNAS nexus", "author": [ { "family": "Jacquerie", "given": "Kathleen" }, { "family": "Tyulmankov", "given": "Danil" }, { "family": "Sacré", "given": "Pierre" }, { "family": "Drion", "given": "Guillaume" } ], "container-title-short": "PNAS Nexus", "volume": "5", "issue": "7", "page": "pgag213", "DOI": "10.1093/pnasnexus/pgag213", "PMID": "42394754", "PMCID": "PMC13323795", "ISSN": "2752-6542", "publisher": "Oxford University Press", "URL": "https://doi.org/10.1093/pnasnexus/pgag213", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 12 ] ] } }

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