OSCR

Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks.

Code ↔ Paper

19 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 19 matches
  1. [1] § Results ↔ Analysis/burst_detection.py, lines 17–119 · score 0.76 · bursting metrics, burst events, bursting rate, Firing rates, bursting activity, MBR
  2. [2] § Materials and Methods › Data analysis › Spiking, bursting, and network bursting analysis ↔ Analysis/burst_detection.py, lines 17–119 · score 0.72 · detected burst events, bursting rate, minute, interval, SpB, MBR
  3. [3] § Materials and Methods › Neuronal network model ↔ Model/Figures/generate_concurrent_topologies.py, lines 1–22 · score 0.70 · power law, outgoing connections, kept, concurrent, graph, Gaussian
  4. [4] § Materials and Methods › Neuronal network model ↔ Model/Main_network_model.ipynb, lines 21–137 · score 0.67 · dependent synaptic delays, synaptic connections, radius, um, distance, cells
  5. [5] § Materials and Methods › Neuronal network model ↔ Model/Figures/Suppl_Multiple_Networks_Idc_no_noise.ipynb, lines 20–156 · score 0.66 · AHP channel, Nernst potentials, voltage, potassium, sodium, adaptation
  6. [6] § Materials and Methods › Neuronal network model ↔ Model/Figures/Suppl_Multiple_Networks_Idc_no_noise.ipynb, lines 20–156 · score 0.66 · short term plasticity, NMDA conductance, ions, voltage, sums, synapses
  7. [7] § Materials and Methods › Neuronal network model ↔ Model/Figures/EPSP_EPSC_simulations.py, lines 14–27 · score 0.62 · membrane capacitance, Nernst potentials, noisy, potassium, sodium, Na
  8. [8] § Materials and Methods › Neuronal network model ↔ Model/Figures/Syn_STD.py, lines 72–84 · score 0.61 · available vesicles, short term depression, STD, synapse, presynaptic, modeled
  9. [9] § Materials and Methods › Data analysis › Principal component analysis ↔ Analysis/principal_component_analysis.ipynb, lines 11–56 · score 0.61 · principal component, transformation, variance, PCA, IFRs, weight
  10. [10] § Materials and Methods › Neuronal network model ↔ Model/Figures/EPSP_EPSC_simulations.py, lines 14–27 · score 0.60 · membrane capacitance, standard deviation, fluctuations, noisy, simulation, neuron
  11. [11] § Materials and Methods › Simulation protocol ↔ Model/Figures/Suppl_Integration_step_noise.ipynb, lines 22–130 · score 0.60 · numerical stability, exponential Euler, accuracy, simulated, dynamics, models
  12. [12] § Materials and Methods › Neuronal network model ↔ Model/Figures/Suppl_pacemakers.ipynb, lines 16–26 · score 0.59 · membrane capacitance, standard deviation, fluctuations, noisy, Neuronal, model
  13. [13] § Materials and Methods › Neuronal network model ↔ Model/Figures/delay_distribution.py, lines 21–37 · score 0.59 · Euclidean distance, SF networks, delay, SW, RND, connectivity
  14. [14] § Results ↔ Model/Figures/FI_curve_noise.py, lines 78–91 · score 0.59 · noise amplitudes, noise driven, Firing rates, curve, MFR, activity
  15. [15] § Materials and Methods › Neuronal network model ↔ Model/Main_network_model.ipynb, lines 21–137 · score 0.58 · membrane potential, NMDA conductance, ions, sums, synapses, channels
  16. [16] § Materials and Methods › Data analysis › Spiking, bursting, and network bursting analysis ↔ Analysis/fittingSTH.py, lines 55–57 · score 0.57 · double exponential, rising phase, curve, STH, fitting
  17. [17] § Results › Topological organization influences the temporal profile of population-wide burst dynamics ↔ Model/Figures/Suppl_Integration_step_noise.ipynb, lines 22–130 · score 0.56 · synaptic depression, synaptic conductance, noise, amplitude, temporal, intrinsic
  18. [18] § Results › Topological organization influences the temporal profile of population-wide burst dynamics ↔ Model/Figures/generate_concurrent_topologies.py, lines 1–22 · score 0.55 · concurrent topologies, outgoing connections, incoming
  19. [19] § Materials and Methods › Data analysis ↔ Model/Main_network_model.ipynb, lines 140–187 · score 0.54 · cumulative instantaneous firing, firing rates, durations, IFR, neuron

Paper

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The authors' code

Jupyter notebook · 187 lines · 7.1 KB · MIT · 3 matches

  1. # %% [markdown]
  2. # ## ================================================================
  3. # ## BRIAN2 SIMULATION OF A RECURRENT EXCITATORY NEURONAL NETWORK
  4. # ## ================================================================
  5. # This notebook simulates a population of excitatory neurons connected through **AMPA** and **NMDA** synapses using the *Brian2* simulator.
  6. # It includes intrinsic Hodgkin–Huxley-like neuron dynamics, synaptic transmission with short-term depression, and spatially dependent connection delays.
  7. # The simulation outputs **raster plots** and **population firing rates (cIFR)**.
  9. # %%
  10. # ------------------------------------------------
  11. # Import required packages
  12. # ------------------------------------------------
  14. !pip install brian2 -q
  15. from brian2 import *
  16. import numpy as np
  17. from time import time
  18. import matplotlib.pyplot as plt
  19. set_device('cpp_standalone', build_on_run=False)
  21. # %%
  22. # ================================================================
  23. # Parameter Setting
  24. # ================================================================
  25. # Define biophysical, synaptic, and network parameters.
  26. # These include Nernst potentials, conductances, time constants,
  27. # network geometry, and noise amplitude.
  29. Ne = 100 # Number of excitatory neurons
  30. El = -39.2 * mV # Nernst potential of leaky ions
  31. EK = -80 * mV # Nernst potential of potassium
  32. ENa = 70 * mV # Nernst potential of sodium
  33. VT = -30.4*mV # alters firing threshold of neurons
  34. E_ampa = 0*mV # reverse synaptic potential
  35. tau_ampa = 2*ms # synaptic time constant AMPA
  36. E_nmda = 0 * mV # Nernst potential of synaptic channels
  37. taud_nmda = 100 * ms # decay time constant of nmda conductance
  38. taur_nmda = 2 * ms # rise time constant of nmda conductance
  39. tau_d = 800 * ms # Recovery time constant of synaptic vesicles
  40. Vmax = 25*mm/second # Axonal conduction velocity
  41. radius = 160 * um # Network spatial radius
  42. fD = 0.0075 # Synaptic depression strength
  43. g_nmda = 0.0275*nS # NMDA synaptic conductance
  44. g_ampa = 0.35*nS # AMPA synaptic conductance
  45. sigma = 5.35*mV # Membrane potential oscillation amplitude
  48. # ================================================================
  49. # Definition of Cell Intrinsic and Synaptic Equations
  50. # ================================================================
  51. # Hodgkin–Huxley-like intrinsic dynamics with leak, Na+, and K+ currents.
  52. # Includes AMPA and NMDA synaptic currents and a stochastic noise term.
  54. eqs = '''
  55. dV/dt = (noise -gl*(V-El) - g_na*(m**3)*h*(V-ENa) - g_kd*(n**4)*(V-EK) - I_syn)/Cm : volt
  56. dm/dt = alpha_m*(1-m)-beta_m*m : 1
  57. dh/dt = alpha_h*(1-h)-beta_h*h : 1
  58. dn/dt = alpha_n*(1-n)-beta_n*n : 1
  59. alpha_m = 0.32*(mV**-1)*4*mV/exprel((13*mV-V+VT)/(4*mV))/ms : Hz
  60. beta_m = 0.28*(mV**-1)*5*mV/exprel((V-VT-40*mV)/(5*mV))/ms : Hz
  61. alpha_h = 0.128*exp((17*mV-V+VT)/(18*mV))/ms : Hz
  62. beta_h = 4./(1+exp((40*mV-V+VT)/(5*mV)))/ms : Hz
  63. alpha_n = 0.032*(mV**-1)*5*mV/exprel((15*mV-V+VT)/(5*mV))/ms : Hz
  64. beta_n = .5*exp((10*mV-V+VT)/(40*mV))/ms : Hz
  65. noise = sigma*(2*gl*Cm)**.5*randn()/sqrt(dt) : amp (constant over dt)
  66. I_syn = I_ampa+I_nmda : amp
  67. I_ampa = g_ampa*(V-E_ampa)*s_ampa : amp
  68. ds_ampa/dt = -s_ampa/tau_ampa : 1
  69. I_nmda = g_nmda*(V-E_nmda)*s_nmda_tot/(1+exp(-0.062*V/mV)/3.57) : amp
  70. s_nmda_tot : 1
  71. x : meter
  72. y : meter
  73. area : meter**2
  74. Cm = (1*uF*cm**-2) * area : farad
  75. g_na = (50*mS*cm**-2) * area : siemens
  76. g_kd = (5*mS*cm**-2) * area : siemens
  77. gl = (0.3*mS*cm**-2) * area : siemens
  78. '''
  80. eqs_synE_model = '''
  81. s_nmda_tot_post = s_nmda * x_d : 1 (summed)
  82. ds_nmda/dt = -s_nmda/(taud_nmda)+x_nmda*(1-s_nmda)/taur_nmda : 1 (clock-driven)
  83. dx_nmda/dt = -x_nmda/(taur_nmda) : 1 (clock-driven)
  84. dx_d/dt = (1-x_d)/tau_d :1 (clock-driven)
  85. '''
  87. eqs_synE_onpre = '''
  88. s_ampa += x_d
  89. x_nmda += 1
  90. x_d *= (1-fD)
  91. '''
  93. # ================================================================
  94. # Network Construction
  95. # ================================================================
  96. # Define neuron group, spatial layout, and synaptic connectivity.
  98. cellsExc = NeuronGroup(Ne, model=eqs, threshold='V>0*mV', refractory=2*ms, method='exponential_euler')
  99. cellsExc.area = 300*um**2
  100. cellsExc.V = El
  102. # Random spatial distribution within circular area
  103. cellsExc.x = np.sqrt(np.random.rand(Ne)) * radius * np.cos(2 * np.pi * np.random.rand(Ne))
  104. cellsExc.y = np.sqrt(np.random.rand(Ne)) * radius * np.sin(2 * np.pi * np.random.rand(Ne))
  106. # Load predefined structural connectivity (source/target pairs)
  107. sources = np.load('source_SF_RND_a2.npy') # to change accordingly
  108. targets = np.load('target_SF_RND_a2.npy') # to change accordingly
  110. # Create excitatory synapses
  111. syn_EE = Synapses(cellsExc, cellsExc, model=eqs_synE_model, on_pre=eqs_synE_onpre, method='euler')
  112. syn_EE.connect(i=sources, j=targets)
  114. # Distance-dependent synaptic delays
  115. syn_EE.delay = '(sqrt((x_pre - x_post)**2 + (y_pre - y_post)**2))/Vmax'
  118. # ================================================================
  119. # Simulation Execution and Spike Train saving
  120. # ================================================================
  121. # Run the simulation for a specified duration and record spike times.
  123. spikes = SpikeMonitor(cellsExc)
  125. dur = 65*second
  126. trans = 5*second
  127. run(dur)
  128. start_time = time()
  129. device.build(run=False)
  130. device.run()
  131. sp_t = spikes.t[spikes.t > trans]-trans
  132. sp_i = spikes.i[spikes.t > trans]
  134. np.save('spikesT.npy', sp_t/second)
  135. np.save('spikesI.npy', sp_i)
  137. print(f'Elapsed time: {(time()-start_time):.1f} seconds')
  140. # %%
  141. # ================================================================
  142. # Analysis and Visualization
  143. # ================================================================
  144. # Compute smoothed cumulative instantaneous firing rate (cIFR)
  145. # and generate raster and rate plots.
  148. def gaussian_window(window_size, sigma):
  149. """Return a normalized Gaussian kernel for smoothing."""
  150. x = np.linspace(-window_size // 2, window_size // 2, window_size)
  151. gauss = np.exp(-0.5 * (x / sigma) ** 2)
  152. return gauss / gauss.sum()
  154. # Parameters
  155. freqSam = 10_000 # Hz
  156. freqSam_sub = 1_000 # Hz
  157. acqTime = int((dur-trans)/second) # s
  158. n_sam = int(freqSam_sub * acqTime)
  159. smooth_window = 100 # ms
  160. smooth_window = int(smooth_window * freqSam_sub / 1000)
  162. # Cumulative IFR
  163. cum_spikes, sp_count = np.unique((sp_t/second*freqSam_sub).astype(int), return_counts=True)
  164. cum_peak = np.zeros(n_sam)
  165. cum_peak[cum_spikes] = sp_count
  166. cIFR = np.convolve(cum_peak, gaussian_window(smooth_window, smooth_window / 6) * freqSam_sub, mode='same')
  168. fig, axes = plt.subplots(2, 1, figsize=(12, 6), sharex=True,
  169. gridspec_kw={'height_ratios': [2, 1]})
  171. # Raster Plot
  172. axes[0].scatter(sp_t/second, sp_i, s=0.05, color='k')
  173. axes[0].set_xlim((0, acqTime))
  174. axes[0].set_ylim((-1, 100))
  175. axes[0].set_xticks([])
  176. axes[0].set_yticks([])
  177. axes[0].set_ylabel('Neuron index')
  179. # cIFR
  180. time_axis = np.arange(n_sam) / freqSam_sub
  181. axes[1].plot(time_axis, cIFR, color='skyblue')
  182. axes[1].set_xlim((0, acqTime))
  183. axes[1].set_xlabel('Time (s)')
  184. axes[1].set_ylabel('cIFR (sp/s)')
  186. fig.tight_layout()
  187. plt.show()

Main_network_model.ipynb at commit f61e17e, under MIT · at the source

Overview

Authors: Valerio Barabino1, Francesca Callegari1, Sergio Martinoia1,2, Paolo Massobrio1,3
ORCID iDs: Valerio Barabino, Francesca Callegari, Sergio Martinoia, Paolo Massobrio
  1. Neuroengineering Genoa Group, Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova 16145, Italy
  2. IRCCS Ospedale Policlinico San Martino, Genova 16132, Italy
  3. National Institute for Nuclear Physics (INFN), Genova 16145, Italy
Institutions: University of Genoa (Italy); Ospedale Policlinico San Martino (Italy); Istituto Nazionale di Fisica Nucleare (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Genova (Italy)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 17, article e0912252026
Dates: received 30 April 2025; accepted 4 March 2026; published online 24 March 2026; in print 29 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.0912-25.2026 · PMID 41876230 · PMCID PMC13129645 · OpenAlex W7140227725
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Machine learning, Single-unit activity, calcium imaging, Spectral & time-frequency
Keywords: connectivity, excitatory neurons, human-induced pluripotent stem cells, in silico network model, in vitro recordings, spontaneous network activity
MeSH: Action Potentials*, Computer Simulation*, Models, Neurological*, Nerve Net*, Neurons*, Cells, Cultured, Humans, Induced Pluripotent Stem Cells (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

This work presents a computational model of excitatory neuronal networks derived from human-induced pluripotent stem cells, whose activity was recorded with microelectrode arrays. A key feature of in vitro neuronal cultures is the emergence of network bursts (NBs)—population events involving most neurons, characterized by different durations, firing frequencies, and recruitment patterns. Our numerical approach investigates the mechanisms underlying these dynamics, addressing the limitations of experimental systems that make it difficult to isolate specific parameters and processes. The model aims to investigate how local neuronal dynamics and global structural connectivity interact to shape the emergence, propagation, and termination of NBs, highlighting the interdependence between intrinsic and network-level mechanisms. We demonstrate the critical role of noise in triggering NBs. At the same time, nonrandom, structured network topologies are essential for sustaining and shaping the resulting collective spatiotemporal firing patterns. In particular, we showed that the organization of incoming and outgoing degrees significantly modulates population recruitment and burst structure, with a hierarchical organization of afferent connectivity emerging as the dominant determinant of collective dynamics. By integrating in vitro observations into in silico simulations, the present study provides a solid foundation for understanding the principles governing human neuronal network function. Also, it sets the stage for investigating how alterations of network properties may contribute to pathological conditions.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

Zenodo 15371493

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (17 files), Matplotlib (13 files), Brian 2 (12 files), SciPy (8 files), scikit-learn (3 files), NetworkX (2 files), SymPy (2 files), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
21 files
At the source: doi.org/10.5281/zenodo.15371493

screenneuropharm/humaninsilicomodel

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f61e17e7b6f1b084c6b5199606d156698cf10988, 4 November 2025
Languages: Python (10), Jupyter (9)
Size: 35 files, 19 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, 9 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (17 files), Matplotlib (13 files), Brian 2 (12 files), SciPy (8 files), scikit-learn (3 files), NetworkX (2 files), SymPy (2 files), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/ScreenNeuroPharm/HumanInSilicoModel/tree/HumanInSilicoModel_v1

Code accessibility

The network model files (Python) and the customized functions (Python) used to analyze both the simulated and experimental (reference) data have been deposited in Zenodo. The DOI of the code reported in this article is 10.5281/zenodo.15371493 (https://doi.org/10.5281/zenodo.15371493).

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

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 8 MeSH terms, 59 references.

Cite

This paper

Barabino, V., Callegari, F., Martinoia, S., & Massobrio, P. (2026). Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(17), e0912252026. https://doi.org/10.1523/jneurosci.0912-25.2026

BibTeX

@article{barabino2026hierarchical,
author = {Barabino, Valerio and Callegari, Francesca and Martinoia, Sergio and Massobrio, Paolo},
title = {{Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = apr,
volume = {46},
number = {17},
pages = {e0912252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.0912-25.2026},
url = {https://doi.org/10.1523/jneurosci.0912-25.2026},
pmid = {41876230},
pmcid = {PMC13129645}
}

RIS

TY - JOUR
AU - Barabino, Valerio
AU - Callegari, Francesca
AU - Martinoia, Sergio
AU - Massobrio, Paolo
TI - Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/04/29
VL - 46
IS - 17
SP - e0912252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.0912-25.2026
UR - https://doi.org/10.1523/jneurosci.0912-25.2026
LA - en
ER -

CSL-JSON

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