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Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons.

Code ↔ Paper

5 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 5 matches
  1. [1] § Results ↔ simulating_neurons/neuron_models/model_utils.py, lines 15–98 · score 0.72 · smaller rat, larger human, rat synapses, human synapses, hybrid, S2
  2. [2] § Methods › Simulations and Resulting Datasets. ↔ simulating_neurons/simulate_neuron.py, lines 1056–1114 · score 0.70 · smoothing sigma, firing rate, stimulated, probe, simulated neuron, duration
  3. [3] § Results ↔ data_release/make_cards.py, lines 145–264 · score 0.69 · human models, rat models, human cortical, rat L5, nonlinearities, passive
  4. [4] § Methods › Synapse Models. ↔ simulating_neurons/neuron_models/model_utils.py, lines 15–98 · score 0.69 · rat NMDA, human NMDA, NMDA conductance, Hybrid, S2, GABAA
  5. [5] § Results ↔ data_release/make_cards.py, lines 145–264 · score 0.56 · membrane potential, biophysical neuron model, prediction, network, somatic, trained

Paper

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The authors' code

Python · 206 lines · 7 KB · no license · 2 matches

  1. from neuron import h,gui
  2. import pandas as pd
  3. import numpy as np
  4. import os
  5. import logging
  6. import sys
  7. import pathlib
  9. sys.path.append(str(pathlib.Path(__file__).parent.parent.parent.parent.parent.parent.absolute()))
  11. from simulating_neurons.neuron_plotter import NeuronPlotter, MAX_CM, MIN_CM
  13. logger = logging.getLogger(__name__)
  15. # The four synapse types of the paper (SI Appendix, Table S2). Two encodings differ from the
  16. # table: conductances are in uS rather than nS, and the NMDA conductance is stored as NMDA_ratio,
  17. # its ratio to the AMPA one - the 1.31 nS of human NMDA is NMDA_ratio * AMPA_default_conductance.
  18. PARAMETER_SETS = {
  19. # "human" in the paper
  20. 'human':{
  21. 'AMPANMDA_e': 0,
  22. 'tau_r_AMPA':0.3,
  23. 'tau_d_AMPA':1.8,
  24. 'tau_r_NMDA':5,
  25. 'tau_d_NMDA':43,
  26. 'gamma':0.078,
  27. 'NMDA_ratio':0.00131/0.00088,
  28. 'AMPA_default_conductance':0.00088,
  30. 'GABAA_e': -80,
  31. 'tau_r_GABAA':0.2,
  32. 'tau_d_GABAA':8,
  33. 'GABAB_ratio': 0,
  34. 'GABAA_default_conductance':0.0007,
  36. 'celsius':34.0,
  37. },
  39. # "hybrid B" in the paper: human synapses with the smaller rat NMDA gamma
  40. 'human_rat_gamma':{
  41. 'AMPANMDA_e': 0,
  42. 'tau_r_AMPA':0.3,
  43. 'tau_d_AMPA':1.8,
  44. 'tau_r_NMDA':5,
  45. 'tau_d_NMDA':43,
  46. 'gamma':0.062,
  47. 'NMDA_ratio':0.00131/0.00088,
  48. 'AMPA_default_conductance':0.00088,
  50. 'GABAA_e': -80,
  51. 'tau_r_GABAA':0.2,
  52. 'tau_d_GABAA':8,
  53. 'GABAB_ratio': 0,
  54. 'GABAA_default_conductance':0.0007,
  56. 'celsius':34.0,
  57. },
  59. # "rat" in the paper
  60. 'rat':{
  61. 'AMPANMDA_e': 0,
  62. 'tau_r_AMPA':0.2,
  63. 'tau_d_AMPA':1.7,
  64. 'tau_r_NMDA':0.29,
  65. 'tau_d_NMDA':43,
  66. 'gamma':0.062,
  67. 'NMDA_ratio':0.0003/0.0004,
  68. 'AMPA_default_conductance':0.0004,
  70. 'GABAA_e': -80,
  71. 'tau_r_GABAA':0.2,
  72. 'tau_d_GABAA':8,
  73. 'GABAB_ratio': 0,
  74. 'GABAA_default_conductance':0.0007,
  76. 'celsius':34.0,
  77. },
  79. # "hybrid A" in the paper: rat synapses with the larger human NMDA gamma
  80. 'rat_human_gamma':{
  81. 'AMPANMDA_e': 0,
  82. 'tau_r_AMPA':0.2,
  83. 'tau_d_AMPA':1.7,
  84. 'tau_r_NMDA':0.29,
  85. 'tau_d_NMDA':43,
  86. 'gamma':0.078,
  87. 'NMDA_ratio':0.0003/0.0004,
  88. 'AMPA_default_conductance':0.0004,
  90. 'GABAA_e': -80,
  91. 'tau_r_GABAA':0.2,
  92. 'tau_d_GABAA':8,
  93. 'GABAB_ratio': 0,
  94. 'GABAA_default_conductance':0.0007,
  96. 'celsius':34.0,
  97. },
  98. }
  100. def create_synapses(cell, parameter_set_name):
  101. params = PARAMETER_SETS[parameter_set_name]
  103. logger.info(f'Creating synapses for parameter set: {parameter_set_name}')
  105. dend_secs = ['dend','apic']
  106. num_segments = 0
  107. all_segments = []
  108. seg_lens = []
  109. for sec in cell.all:
  110. if sum([1 for i in dend_secs if i in sec.name()]):
  111. num_segments+= sec.nseg
  112. for seg in sec:
  113. all_segments.append(seg)
  114. seg_lens.append(seg.sec.L/sec.nseg)
  117. # Create excitatory and inhibitory synapses per segment
  118. exc_synapses = []
  119. exc_netcons = []
  120. inh_synapses = []
  121. inh_netcons =[]
  122. for seg in all_segments:
  123. if 'old_impl' in params and params['old_impl']:
  124. AMPANMDA = h.ProbAMPANMDA2(seg)
  125. AMPANMDA.tau_r_AMPA = params['tau_r_AMPA']
  126. AMPANMDA.tau_d_AMPA = params['tau_d_AMPA']
  127. AMPANMDA.tau_r_NMDA = params['tau_r_NMDA']
  128. AMPANMDA.tau_d_NMDA = params['tau_d_NMDA']
  129. if 'old_weight' in params and params['old_weight']:
  130. AMPANMDA.gmax = params['AMPA_default_conductance']
  131. else:
  132. AMPANMDA.gmax = 1
  133. AMPANMDA.e = params['AMPANMDA_e']
  134. AMPANMDA.Use = 1
  135. AMPANMDA.u0 = 0
  136. AMPANMDA.Dep = 0
  137. AMPANMDA.Fac = 0
  138. AMPANMDA_ncon = h.NetCon(None, AMPANMDA)
  139. if 'old_weight' in params and params['old_weight']:
  140. AMPANMDA_ncon.weight[0] = 1
  141. else:
  142. AMPANMDA_ncon.weight[0] = params['AMPA_default_conductance']
  143. else:
  144. AMPANMDA = h.AMPANMDA_EMS(seg)
  145. AMPANMDA.e = params['AMPANMDA_e']
  146. AMPANMDA.tau_r_AMPA = params['tau_r_AMPA']
  147. AMPANMDA.tau_d_AMPA = params['tau_d_AMPA']
  148. AMPANMDA.tau_r_NMDA = params['tau_r_NMDA']
  149. AMPANMDA.tau_d_NMDA = params['tau_d_NMDA']
  150. AMPANMDA.gamma = params['gamma']
  151. AMPANMDA.NMDA_ratio = params['NMDA_ratio']
  152. AMPANMDA_ncon = h.NetCon(None, AMPANMDA)
  153. AMPANMDA_ncon.weight[0] = params['AMPA_default_conductance']
  155. exc_synapses.append(AMPANMDA)
  156. exc_netcons.append(AMPANMDA_ncon)
  158. # for naming, it is better to run it twice
  159. for seg in all_segments:
  160. if 'old_impl' in params and params['old_impl']:
  161. GABAAB = h.ProbUDFsyn2(seg)
  162. GABAAB.tau_r = params['tau_r_GABAA']
  163. GABAAB.tau_d = params['tau_d_GABAA']
  164. GABAAB.e = params['GABAA_e']
  165. if 'old_weight' in params and params['old_weight']:
  166. GABAAB.gmax = params['GABAA_default_conductance']
  167. else:
  168. GABAAB.gmax = 1
  169. GABAAB.Use = 1
  170. GABAAB.u0 = 0
  171. GABAAB.Dep = 0
  172. GABAAB.Fac = 0
  173. GABAAB_ncon = h.NetCon(None, GABAAB)
  174. if 'old_weight' in params and params['old_weight']:
  175. GABAAB_ncon.weight[0] = 1
  176. else:
  177. GABAAB_ncon.weight[0] = params['GABAA_default_conductance']
  178. else:
  179. GABAAB = h.GABAAB_EMS(seg)
  180. GABAAB.e_GABAA = params['GABAA_e']
  181. GABAAB.tau_r_GABAA = params['tau_r_GABAA']
  182. GABAAB.tau_d_GABAA = params['tau_d_GABAA']
  183. GABAAB.GABAB_ratio = params['GABAB_ratio']
  184. GABAAB_ncon = h.NetCon(None, GABAAB)
  185. GABAAB_ncon.weight[0] = params['GABAA_default_conductance']
  187. inh_synapses.append(GABAAB)
  188. inh_netcons.append(GABAAB_ncon)
  190. syns = pd.DataFrame({'segments':all_segments,'seg_lens':seg_lens, 'exc_synapses':exc_synapses, 'exc_netcons':exc_netcons,
  191. 'inh_synapses':inh_synapses, 'inh_netcons':inh_netcons})
  193. # sort segments by the average y coordinate of the segment, related to the default projection of the neuron
  194. neuron_plotter = NeuronPlotter(cell, list(syns['segments']))
  195. seg_id_to_average_seg_y = {}
  196. for seg_id, seg in enumerate(syns['segments']):
  197. seg_id_to_average_seg_y[seg_id] = neuron_plotter.get_seg_coord(seg_id)[1].mean()
  198. average_seg_ys = np.array([seg_id_to_average_seg_y[seg_id] for seg_id in range(len(syns['segments']))])
  199. sorted_according_to_average_seg_y = np.argsort(average_seg_ys) # segment indices by color
  200. syns = syns.reindex(sorted_according_to_average_seg_y)
  201. syns = syns.reset_index(drop=True)
  203. logger.info(f"Setting temperature to be {params['celsius']} degree celsius")
  204. h.celsius = params['celsius']
  206. return syns

model_utils.py at commit 75ad8b4, no license · at the source

Overview

Authors: Ido Aizenbud1, Daniela Yoeli1, David Beniaguev1, Christiaan P. J. de Kock2, Michael London1,3, Idan Segev1,3
ORCID iDs: Ido Aizenbud, Daniela Yoeli, David Beniaguev, Michael London, Idan Segev
  1. The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
  2. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
  3. Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Institutions: Hebrew University of Jerusalem (Israel); Vrije Universiteit Amsterdam (Netherlands)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 28, article e2533168123
Dates: received 16 November 2025; accepted 15 May 2026; published online 7 July 2026; in print 14 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2533168123 · PMID 42412934 · PMCID PMC13367794 · OpenAlex W7167623350
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), rat (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning, Single-unit activity, calcium imaging
Keywords: single neuron computation, dendritic computation, functional complexity, human neurons, cortical pyramidal neurons
MeSH: Cerebral Cortex*, Dendrites*, Neurons*, Pyramidal Cells*, Synapses*, Animals, Female, Humans, Models, Neurological, Rats, Receptors, N-Methyl-D-Aspartate (* major topic)
Journal subjects: Biological Sciences, Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: ONR (N00014-24-1-2055, N00014-23-1-2051); Israel Science Foundation (ISF) (1331/23); HUJI | National Institute for Psychobiology in Israel, Hebrew University of Jerusalem (NIPI) (206-22-23); United States-Israel Binational Science Foundation (BSF) (2023104); NIH (1RM1NS132981-01)
Citations: not cited yet (Europe PMC); 79 references in the paper
Research resources: RRID:SCR_014806

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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ido4848/fci

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 75ad8b4d81a7f51bf888b30650c543592340db06, 19 August 2026
Languages: NEURON (97), Python (72), Shell (1)
Size: 271 files, 170 scripts
Software Heritage: not archived
Found in: the references
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (124 files), pandas (27 files), NumPy (12 files), Matplotlib (6 files), h5py (5 files), PyTorch (4 files), SciPy (3 files), Numba (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
171 files
At the source: github.com/ido4848/fci

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

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Read it in the paper: doi.org/10.1073/pnas.2533168123.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 5 funders, 67 references, 1 RRID.

Cite

This paper

Aizenbud, I., Yoeli, D., Beniaguev, D., de Kock, C. P. J., London, M., & Segev, I. (2026). Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons. Proceedings of the National Academy of Sciences of the United States of America, 123(28), e2533168123. https://doi.org/10.1073/pnas.2533168123

BibTeX

@article{aizenbud2026dendritic,
author = {Aizenbud, Ido and Yoeli, Daniela and Beniaguev, David and de Kock, Christiaan P. J. and London, Michael and Segev, Idan},
title = {{Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = jul,
volume = {123},
number = {28},
pages = {e2533168123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2533168123},
url = {https://doi.org/10.1073/pnas.2533168123},
pmid = {42412934},
pmcid = {PMC13367794}
}

RIS

TY - JOUR
AU - Aizenbud, Ido
AU - Yoeli, Daniela
AU - Beniaguev, David
AU - de Kock, Christiaan P. J.
AU - London, Michael
AU - Segev, Idan
TI - Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/07/07
VL - 123
IS - 28
SP - e2533168123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2533168123
UR - https://doi.org/10.1073/pnas.2533168123
LA - en
ER -

CSL-JSON

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"title": "Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons",
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