Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms.

Code ↔ Paper

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The 10 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted

[1] § Results › Comparison with in vitro experiments ↔ scripts/fig4_slow_potassium.py, lines 1 to 29 · score 0.82 · shorter bursting period, isolated bursts, extracellular potassium, slow potassium, vitro, LFP · Report a mismatch
[2] § Results › Biophysically inspired mean-field model ↔ scripts/figS1_bimodal_and_psd.py, lines 1 to 19 · score 0.79 · power spectral density, supra threshold activity, nullcline geometry, parabola coefficients, bimodal, membrane potential · Report a mismatch
[3] § Results › Biophysically inspired mean-field model ↔ scripts/fig2_single_neuron.py, lines 32 to 68 · score 0.77 · sustained ictal activity, depolarization block, tonic spiking, single neuron, events, seizure · Report a mismatch
[4] § Results › Coupled neural masses ↔ scripts/fig6_network.py, lines 1 to 28 · score 0.68 · global coupling, pathological activity, structural connections, neural masses, weights, node · Report a mismatch
[5] § Results › Comparison with neural network simulations ↔ scripts/fig2_single_neuron.py, lines 32 to 68 · score 0.63 · depolarization block, tonic spiking, single neuron, events, regimes, seizure · Report a mismatch
[6] § Results › Comparison with neural network simulations ↔ ionmf/population.py, lines 1 to 71 · score 0.63 · synaptic strength, heterogeneous excitability, coupled HH, single neuron, width, network · Report a mismatch
[7] § Results › Comparison with in vitro experiments ↔ scripts/fig4_slow_potassium.py, lines 1 to 29 · score 0.61 · rescaled parameters, extracellular potassium, shorter, isolated, vitro, LFP · Report a mismatch
[8] § Materials and methods › Biophysical neuron model ↔ ionmf/single_neuron.py, lines 1 to 14 · score 0.59 · ion exchange, gating variable, variation, buffering, membrane potential, intracellular · Report a mismatch
[9] § Results › Coupled neural masses ↔ scripts/fig6_network.py, lines 1 to 28 · score 0.54 · pathological activity, structural connectivity, propagate, weighted, bursting, network · Report a mismatch
[10] § Results › Biophysically inspired mean-field model ↔ ionmf/__init__.py, the whole file · a weak match · score 0.54 · Hodgkin Huxley, nullcline geometry, coefficients, inspired, Neuronal, parabola · Report a mismatch

Paper

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The authors' code

Python · 80 lines · 3.8 KB · MIT · 2 matches

#!/usr/bin/env python
"""Figure 4 -- slow extracellular-potassium oscillations with bursts on top.
(a) mean-field model with slow ion dynamics: C_m = 16, tau_n = 8,
epsilon = 1e-4, gamma = 2.5e-4, J = 0.01, R- = 0.001, R+ = -0.01
([K+]_bath = 15.5, 15 min of simulated time; the last 12 min are shown)
(b) network of N = 3000 HH-type neurons with rescaled parameters
(C_m = 10, tau_n = 2.4, J = 1, Delta = 1, [K+]_bath = 15.5) -- shorter
bursting period, same qualitative picture
(c, d) in vitro LFP + [K+]_ext recordings: plotted if csv files are found in
data/in_vitro/ (see data/in_vitro/README.md), otherwise skipped
(e) an emergent mean-field regime with isolated bursts in the up state
(J = 2, R- = 0.5, R+ = -0.5, [K+]_bath = 15.5; Supplementary File 1,
column labelled "Fig. 5c" in the preprint numbering)
"""
import glob
import os
import matplotlib.pyplot as plt
import numpy as np
from _common import parser, cached, DATA_DIR
from ionmf import population as pop, mean_field as mf, params as P
from ionmf.plotting import strip, save, scalebar, V_COLOR, K_COLOR
p = parser(__doc__)
p.add_argument("--N", type=int, default=3000)
args = p.parse_args()
N = args.N if not args.quick else 300
def two_traces(t, V, K, title, bar_s, name):
fig, (a1, a2) = plt.subplots(2, 1, figsize=(3.2, 1.9), sharex=True)
a1.plot(t, V, color=V_COLOR, lw=0.5); a1.set_ylabel("V"); a1.set_title(title)
a2.plot(t, K, color=K_COLOR, lw=0.8); a2.set_ylabel("$[K^+]_{ext}$")
strip(a1); strip(a2)
scalebar(a2, t[0], bar_s, f"{bar_s:g} s" if bar_s < 60 else f"{bar_s/60:g} min")
save(fig, args.out, name)
# ---------------------------------------------------------------- panel a
par_a = P.make(Cm=16.0, tau_n=8.0, gamma=0.00025, epsilon=0.0001)
coef_a = P.parabola(R_minus=0.001, R_plus=-0.01)
dur_a = 15 * 60_000.0 if not args.quick else 3 * 60_000.0
sa = cached(f"fig4a_mf_slow_{dur_a:g}", lambda: mf.simulate(
15.5, J=0.01, eta=0.0, Delta=1.0, E=0.0, z0=(36.8, -70.8, 0.053, -0.231, 7.93),
duration_ms=dur_a, dt_ms=1.0, coef=coef_a, par=par_a), not args.no_cache)
n_show = int(len(sa["t"]) * 12 / 15)
two_traces(sa["t"][-n_show:] / 1000, sa["V"][-n_show:], sa["Kext"][-n_show:],
"Mean-field simulation", 120, "Fig4a_meanfield_slow")
# ---------------------------------------------------------------- panel b
par_b = P.make(Cm=10.0, tau_n=2.4)
dur_b = 20_000.0 if not args.quick else 4_000.0
sb = cached(f"fig4b_pop_N{N}_T{dur_b:g}", lambda: pop.simulate(
N=N, K_bath=15.5, J=1.0, Delta=1.0, eta_bar=0.0, E=0.0, Vth=-50.0,
duration_ms=dur_b, dt_ms=0.01, z0=(-81.78, 0.03, -4.92, -14.88), par=par_b,
seed=0, report="text"), not args.no_cache)
cut = int(len(sb["t"]) * 0.2)
two_traces(sb["t"][cut:] / 1000, sb["V_mean"][cut:], sb["Kext_mean"][cut:],
"Neural network simulation", 1, "Fig4b_network_rescaled")
# ---------------------------------------------------------------- panels c, d
files = sorted(glob.glob(os.path.join(DATA_DIR, "in_vitro", "*.csv")))
if not files:
print("panels c-d: no in vitro csv found in data/in_vitro/ -- skipped")
for f in files:
d = np.genfromtxt(f, delimiter=",", names=True)
name = os.path.splitext(os.path.basename(f))[0]
two_traces(d["time_s"], d["lfp"], d["k_ext_mM"], f"In-vitro experiment ({name})",
60 if d["time_s"][-1] > 300 else 5, f"Fig4cd_invitro_{name}")
# ---------------------------------------------------------------- panel e
coef_e = P.parabola(R_minus=0.5, R_plus=-0.5)
se = cached("fig4e_mf_emergent_J2", lambda: mf.simulate(
15.5, J=2.0, eta=0.0, Delta=1.0, E=0.0, z0=(0.1, -15.0, 0.45, -5.0, -16.0),
duration_ms=20_000.0, dt_ms=1.0, coef=coef_e), not args.no_cache)
sl = slice(-6000, None)
two_traces(se["t"][sl] / 1000, se["V"][sl], se["Kext"][sl], "Mean-field simulation", 6,
"Fig4e_meanfield_emergent")

fig4_slow_potassium.py at commit 681e45c, under MIT · at the source

Overview

Authors: Giovanni Rabuffo¹, Abhirup Bandyopadhyay¹, Carmela Calabrese¹, Kashyap Gudibanda¹, Damien Depannemaecker¹, Lavinia Mitiko Takarabe¹, Sourin Chatterjee¹, Maria Luisa Saggio¹, Mathieu Desroches^2,3, Anton Ivanov¹, Marja-Leena Linne⁴, Christophe Bernard¹, Spase Petkoski¹, Viktor Jirsa¹

ORCID iDs: Giovanni Rabuffo, Mathieu Desroches, Marja-Leena Linne, Christophe Bernard, Spase Petkoski, Viktor Jirsa

Institutions: Aix-Marseille Université (France); Université de Montpellier (France); Basque Center for Applied Mathematics (Spain); Tampere University (Finland)

Journal: eLife, volume 14, article RP104249

Dates: published online 30 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.104249 · PMID 42814608 · PMCID PMC13626649 · OpenAlex W4407103898

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism), epilepsy (population), computational (subfield)

Methods: Single-unit activity, calcium imaging

Keywords: Hodgkin–Huxley-type neurons, biophysical neural mass, epilepsy, multiscale mechanisms, mean-field model, Mouse

MeSH: Brain*, Models, Neurological*, Neurons*, Action Potentials, Animals, Computer Simulation, Ions, Nerve Net (* major topic)

Journal subjects: Neuroscience

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

Funding: European Commission (10.3030/945539, 10.3030/826421, 10.3030/101137289, 10.3030/101147319, 10.3030/101199894); Agence Nationale de la Recherche (10.67599/anr-22-pesn-0012, 10.67599/anr-24-rrii-0005, ANR-17-CE37-0001-01, ANR-20-NEUC-0005-02); Eusko Jaurlaritza (BERC 2022-2025); Agencia Estatal de Investigación (BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIU / AEI / 10.13039/501100011033)

Citations: not cited yet (Europe PMC); 122 references in the paper

Abstract

Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to include certain parameters that significantly impact brain function. The concentration of ions in the extracellular space is one key factor to consider, as its fluctuations can be associated with healthy and pathological brain states. In this paper, we develop a new mean-field model of a population of Hodgkin–Huxley-type neurons, retaining a microscopic perspective on the ion-exchange mechanisms driving neuronal activity. This allows us to maintain biophysical interpretability while bridging the gap between micro- and macro-scale mechanisms. Our model is able to reproduce a wide range of activity patterns, also observed in large neural network simulations. Specifically, slow-changing ion concentrations modulate the fast neuroelectric activity, a feature of our model that we validated through in vitro experiments. By studying how changes in extracellular ionic conditions can affect whole-brain dynamics, this model serves as a foundation to measure biomarkers of pathological activity and provide potential therapeutic targets in cases of brain dysfunctions like epilepsy.

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 10 matches between paragraphs and lines of code.

grabuffo/ion-exchange-neural-mass

License: MIT

State: the link answers, verified on 3 October 2026

Evidence: files inventoried

Commit: 681e45cf501afbd7be82ffd86be52ae02da20fe9, 22 September 2026

Languages: Python (16), Shell (1)

Size: 62 files, 17 scripts

Software Heritage: archived

Found in: “Data availability”

Holds: README, license file, environment (pyproject.toml, requirements.txt)

Not found: CITATION.cff, tests, continuous integration, documentation

Tools: NumPy (13 files), Matplotlib (7 files), SciPy (5 files), Brian 2 (1 file), NetworkX (1 file), The Virtual Brain (1 file)

Availability: 1 check, the latest on 3 October 2026: the link answers

3 October 2026: the link answers

19 files

ionmf/__init__.py, Python, 16 lines, 1 match
ionmf/mean_field.py, Python, 59 lines
ionmf/network.py, Python, 108 lines
ionmf/nullcline.py, Python, 70 lines
ionmf/params.py, Python, 71 lines
ionmf/plotting.py, Python, 43 lines
ionmf/population.py, Python, 142 lines, 1 match
ionmf/single_neuron.py, Python, 70 lines, 1 match
run_all.sh, Shell, 12 lines
scripts/_common.py, Python, 75 lines
scripts/fig2_single_neuron.py, Python, 120 lines, 2 matches
scripts/fig3_population_vs_meanfield.py, Python, 107 lines
scripts/fig4_slow_potassium.py, Python, 80 lines, 2 matches
scripts/fig6_network.py, Python, 57 lines, 2 matches
scripts/figS1_bimodal_and_psd.py, Python, 68 lines, 1 match
scripts/figS2_first_moment_error.py, Python, 85 lines
tvb/model_HH_ABH.py, Python, 320 lines
LICENSE, License, 21 lines
README.md, Text, 112 lines

At the source: github.com/grabuffo/ion-exchange-neural-mass

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

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Data

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

Data availability

All code and data used in this study are publicly available on GitHub at https://github.com/grabuffo/ion-exchange-neural-mass, copy archived at Rabuffo, 2026.

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

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Version 1, 3 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 6 keywords, 8 MeSH terms, 4 funders, 120 references.

Cite

This paper

Rabuffo, G., Bandyopadhyay, A., Calabrese, C., Gudibanda, K., Depannemaecker, D., Takarabe, L. M., Chatterjee, S., Saggio, M. L., Desroches, M., Ivanov, A., Linne, M.-L., Bernard, C., Petkoski, S., & Jirsa, V. (2026). Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms. eLife, 14, RP104249. https://doi.org/10.7554/elife.104249

BibTeX

@article{rabuffo2026biophysically, author = {Rabuffo, Giovanni and Bandyopadhyay, Abhirup and Calabrese, Carmela and Gudibanda, Kashyap and Depannemaecker, Damien and Takarabe, Lavinia Mitiko and Chatterjee, Sourin and Saggio, Maria Luisa and Desroches, Mathieu and Ivanov, Anton and Linne, Marja-Leena and Bernard, Christophe and Petkoski, Spase and Jirsa, Viktor}, title = {{Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms}}, journal = {eLife}, year = {2026}, month = sep, volume = {14}, pages = {RP104249}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.104249}, url = {https://doi.org/10.7554/elife.104249}, pmid = {42814608}, pmcid = {PMC13626649} }

RIS

TY - JOUR AU - Rabuffo, Giovanni AU - Bandyopadhyay, Abhirup AU - Calabrese, Carmela AU - Gudibanda, Kashyap AU - Depannemaecker, Damien AU - Takarabe, Lavinia Mitiko AU - Chatterjee, Sourin AU - Saggio, Maria Luisa AU - Desroches, Mathieu AU - Ivanov, Anton AU - Linne, Marja-Leena AU - Bernard, Christophe AU - Petkoski, Spase AU - Jirsa, Viktor TI - Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms T2 - eLife J2 - eLife PY - 2026 DA - 2026/09/30 VL - 14 SP - RP104249 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.104249 UR - https://doi.org/10.7554/elife.104249 LA - en ER -

CSL-JSON

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