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Dendritic shaft constrictions shape synaptic integration in neurons.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Realistic compartmental modeling ↔ src/ball_cell.py, lines 12 to 75 · score 0.75 · membrane capacitance, axial resistance, ball, cm2, stick, Realistic · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Morphological analysis of cortical pyramidal neurons reconstructed by serial EM ↔ notebooks/Serial_EM_Cortical_DSC_864691135693733567.ipynb, lines 260 to 314 · score 0.55 · MICrONS, dendritic shaft, cortical, distance, serial, diameter · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Modeling ↔ src/mod/simplenmdaonset.mod, the whole file · a weak match · score 0.54 · 0.06 mV, biexponential, eta, gamma, decay, rise · Report a mismatch
  4. [4] § MATERIALS AND METHODS › Modeling ↔ notebooks/DSC_model.ipynb, lines 101 to 108 · score 0.51 · unitary synaptic conductance, EPSP ratio, S8C, model, DSCs · Report a mismatch

Paper

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The authors' code

Python · 114 lines · 4.2 KB · no license · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Tue Feb 11 14:24:01 2020
  5. @author: Tony Kelly
  6. """
  7. ##load dll maunally in console
  8. #from neuron import h, gui #gui needed for cvode in run
  9. # h.nrn_load_dll("M:\\Tony\\Bonn\\model\\DSC\\mod\\nrnmech.dll") #"F:\\Bonn\\model\\DSC\\mod\\nrnmech.dll"
  11. from neuron import h
  12. class BallAndStick:
  13. def __init__(self, pos=0.88): #0.88 * 25 = 220um
  14. #"""anything that should be done every time one of these is created goes here"""
  15. self.soma = h.Section(name='soma')
  16. self.dend = h.Section(name='dend')
  17. self.dend.connect(self.soma)
  19. self.soma.L = self.soma.diam = 20
  20. self.dend.L = 250
  21. self.dend.nseg = 250
  22. self.dend.diam = 1
  24. self.synapse = [] #without the list new instances of the point process with the same name are overwitten in python and get uns
  26. self.all = [self.soma, self.dend]
  27. for sec in self.all: # <-- NEW
  28. sec.Ra = 181 # Axial resistance in Ohm * cm orig 181 also tried 200 for unitary EPSP # <-- NEW
  29. sec.cm = 1 # Membrane capacitance in micro Farads / cm^2 # <-- NEW
  31. self.soma.insert('pas')
  33. for seg in self.soma:
  34. seg.pas.g = 0.0004 # Passive conductance in S/cm2 orig 0.0002 changed to 0.0004 to achieve attenuation RK saw in realistic cell # <-- NEW
  35. seg.pas.e = -70
  36. self.dend.insert('pas')
  38. for seg in self.dend:
  39. seg.pas.g = 0.0002 # Passive conductance in S/cm2 orig 0.0002 # <-- NEW
  40. seg.pas.e = -70
  41. #self.mksyn(self.dend, pos)
  43. def mksyn(self, loc, pos, NMDA_ratio = 0):
  44. syA = h.SimpleAMPAonset(loc(pos))
  45. syA.onset = 10
  46. syA.tau1 = 2 # 10-20 orig 2 but 40 in mod increased value prolongs opeing larger response and slower decay tried 5
  47. syA.tau2 = 0.05 #1-2 orig 0.05 but 0.33 in mod increased value decreases rise time
  48. syA.gmax = 0.00025 #uS #Mitchell and Williams, Nature Neuroscience, 2008 used <25nS
  49. syA.e = 0
  51. syAI_vec = h.Vector() # Membrane current vector (nA)
  52. syAI_vec.record(syA._ref_i)
  54. self.syA = syA
  55. self.syA_vec = syAI_vec
  57. syN = h.SimpleNMDAonset(loc(pos))
  58. syN.onset = syA.onset
  59. #syN.gmax = syA.gmax*1.15 #uS #Mitchell and Williams, Nature Neuroscience, 2008 used <25nS
  61. syN.gmax = syA.gmax*NMDA_ratio
  62. syN.e = 0
  63. syN.mg = 2
  64. syN.eta = 0.05 #orig mod file 0.33 but Roland 0.05 - shifts relief of Mg block to left
  65. syN.gamma = 0.06 #orig 0.06 increase steepens Mg relief curve
  66. #syN.shift = -10
  68. syNg_vec = h.Vector() # Membrane current vector (nA)
  69. syNg_vec.record(syN._ref_g)
  71. self.syN = syN
  72. self.syN_vec = syNg_vec
  73. self.NMDA_ratio = NMDA_ratio # Only change NMDA_ratio through set_nmda
  75. self.synapse.append((syA,syN))
  78. """
  79. def run_DSC_Ca(cell='GC', pos = 220/250, NMDA_ratio = 1.1):
  80. # print(pos)
  81. cell = BallAndStick()
  82. # cell.dend(200/250).diam = 0.1
  83. cell.soma.insert('cadifus')
  84. cell.soma.insert('capr')
  85. cell.dend.insert('cadifus')
  86. cell.dend.insert('capr')
  88. cell.mksyn(cell.dend, pos, NMDA_ratio)
  90. run_conductance_Ca(cell, pos, NMDA_ratio)
  92. cell.dend(200/250).diam = 0.2
  93. # run_conductance_Ca(cell, pos, NMDA_ratio)
  95. cell.dend(190/250).diam = 0.2
  96. # run_conductance_Ca(cell, pos, NMDA_ratio)
  98. cell.dend(180/250).diam = 0.2
  99. #run_conductance_Ca(cell, pos, NMDA_ratio)
  101. cell.dend(170/250).diam = 0.2
  102. #run_conductance_Ca(cell, pos, NMDA_ratio)
  104. cell.dend(160/250).diam = 0.2
  105. # run_conductance_Ca(cell, pos, NMDA_ratio)
  107. cell.dend(150/250).diam = 0.2
  108. #run_conductance_Ca(cell, pos, NMDA_ratio)
  110. cell.dend(140/250).diam = 0.2
  111. #cell.syA.gmax = i*.00020 #with 7DSC 0.00020 distal proximal
  112. #cell.syN.gmax = cell.syA.gmax*NMDA_ratio
  113. run_conductance_Ca(cell, pos, NMDA_ratio)
  114. """

ball_cell.py at commit cf9df64, no license · at the source

Overview

Authors: Tony Kelly1, Michael Döngi2, Juan Eduardo Rodriguez-Gatica1,3, Netanel Ofer4, Carlos Wert-Carvajal1, Niclas Cissewski1, Michela Barboni1, Philipp Bethge5,6, Christin M Godale7, Sarah Yaser7, Michel K Herde8,9, Jens Tillmann1, Sabrina Ingrid Peter10, Sebastian Dupraz11,12, Henner Koch10, Valentin Stein2, Frank Bradke11, Steve C Danzer7,13, Tatjana Tchumatchenko1, Martin K Schwarz1, Ulrich Kubitscheck3, U Valentin Nägerl5,14, Heinz Beck1,15
ORCID iDs: Tony Kelly, Michael Döngi, Juan Eduardo Rodriguez-Gatica, Netanel Ofer, Philipp Bethge, Sarah Yaser, Michel K Herde, Jens Tillmann, Sabrina Ingrid Peter, Sebastian Dupraz, Henner Koch, Valentin Stein, Frank Bradke, Steve C Danzer, Tatjana Tchumatchenko, Ulrich Kubitscheck, U Valentin Nägerl, Heinz Beck
15 affiliations
  1. Institute for Experimental Epileptology and Cognition Research, Medical Faculty, University of Bonn, Bonn, Germany
  2. Institut für Physiologie II, Universität Bonn, Bonn, Germany
  3. Clausius-Institute of Physical and Theoretical Chemistry, University of Bonn, Bonn, Germany
  4. Department of Electrical and Electronics Engineering, Ariel University, Ariel, Israel
  5. Interdisciplinary Institute for Neuroscience, UMR 5297 CNRS, Université de Bordeaux, Bordeaux, France
  6. Brain Research Institute, University of Zurich, Zurich, Switzerland
  7. Department of Anesthesia, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
  8. Institute of Cellular Neurosciences, Medical Faculty, University of Bonn, Bonn, Germany
  9. Centre for Neuroendocrinology, Department of Physiology, University of Otago, Dunedin, New Zealand
  10. Department of Neurology, Department of Epileptology, RWTH Uniklinik, RWTH Aachen, Aachen, Germany
  11. Axonal Growth and Regeneration Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
  12. Institute for Neurovascular Cell Biology, Faculty of Medicine, University of Bonn, Bonn, Germany
  13. Department of Anesthesiology, University of Cincinnati, Cincinnati, OH, USA
  14. Institut für Anatomie und Zellbiologie, Universitätsmedizin Göttingen, Göttingen, Germany
  15. Deutsches Zentrum für Neurodegenerative Erkrankungen e.V., Bonn, Germany
Institutions: University of Bonn (Germany); Ariel University (Israel); Centre National de la Recherche Scientifique (France); Université de Bordeaux (France); University of Zurich (Switzerland); Institut Interdisciplinaire de Neuroscience (France); Cincinnati Children's Hospital Medical Center (United States); University of Otago (New Zealand); Universitätsklinikum Aachen (Germany); RWTH Aachen University (Germany); German Center for Neurodegenerative Diseases (Germany); University of Cincinnati (United States); Universitätsmedizin Göttingen (Germany); University of Göttingen (Germany)
Journal: Science advances, volume 12, issue 37, article eaec4911
Dates: received 22 September 2025; accepted 30 July 2026; published online 11 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aec4911 · PMID 42726865 · PMCID PMC13564900 · OpenAlex W7212316371
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Preprocessing, Evoked potentials, Graphs, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Dendrites*, Neurons*, Synapses*, Animals, Hippocampus, Humans, Mice (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS062806)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

For nearly 150 years, textbooks have described dendritic morphology as optimized for efficient synaptic voltage transfer from spines to the soma, implemented as a tubular design respecting Rall’s 3/2 rule for impedance matching at branch points. Here, we reveal that this view is an oversimplification. Using multiple high-resolution imaging techniques, we demonstrate that dendrites in cortical and hippocampal neurons exhibit nanoscale constrictions, termed dendritic shaft constrictions (DSCs), with diameters ranging from ∼100 to 500 nanometers in mice. We also identified DSCs in human hippocampal and cortical neurons. We provide theoretical and experimental lines of evidence that these constrictions effectively partition the dendrite into distinct electrical compartments, shaping dendritic integration of synaptic potentials.

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 4 matches between paragraphs and lines of code.

tonykelly00/DSC_model

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: cf9df6483dbb35d16b5715988a5b93e7723cedbe, 21 May 2026
Languages: Python (8), C (6), NEURON (5), Jupyter (1)
Size: 34 files, 20 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, environment (pyproject.toml, uv.lock), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NEURON (10 files), Matplotlib (6 files), NumPy (5 files), pandas (4 files), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
21 files
At the source: github.com/tonykelly00/DSC_model

tonykelly00/DSC_morphology_pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 018caebc0d140fb2bc1e5fcc585a97de84d90ad7, 21 May 2026
Languages: Jupyter (9), Python (4)
Size: 33 files, 13 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, environment (environment.yml), 6 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (11 files), pandas (11 files), NumPy (10 files), SciPy (8 files), seaborn (8 files), statsmodels (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
14 files
At the source: github.com/tonykelly00/DSC_morphology_pipeline

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

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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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 33 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and the Supplementary Materials. This study did not generate new materials. Data used to generate the figures are available at Zenodo (https://zenodo.org/records/14037025). Code used to analyze the data is also available in the Zenodo repository and GitHub (https://github.com/tonykelly00/DSC_morphology_pipeline), and code for the ball and stick model is available on GitHub (https://github.com/tonykelly00/DSC_model)

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 23 authors, 7 MeSH terms, 1 funder, 49 references.

Cite

This paper

Kelly, T., Döngi, M., Rodriguez-Gatica, J. E., Ofer, N., Wert-Carvajal, C., Cissewski, N., Barboni, M., Bethge, P., Godale, C. M., Yaser, S., Herde, M. K., Tillmann, J., Peter, S. I., Dupraz, S., Koch, H., Stein, V., Bradke, F., Danzer, S. C., Tchumatchenko, T., . . . Beck, H. (2026). Dendritic shaft constrictions shape synaptic integration in neurons. Science advances, 12(37), eaec4911. https://doi.org/10.1126/sciadv.aec4911

BibTeX

@article{kelly2026dendritic,
author = {Kelly, Tony and Döngi, Michael and Rodriguez-Gatica, Juan Eduardo and Ofer, Netanel and Wert-Carvajal, Carlos and Cissewski, Niclas and Barboni, Michela and Bethge, Philipp and Godale, Christin M and Yaser, Sarah and Herde, Michel K and Tillmann, Jens and Peter, Sabrina Ingrid and Dupraz, Sebastian and Koch, Henner and Stein, Valentin and Bradke, Frank and Danzer, Steve C and Tchumatchenko, Tatjana and Schwarz, Martin K and Kubitscheck, Ulrich and Nägerl, U Valentin and Beck, Heinz},
title = {{Dendritic shaft constrictions shape synaptic integration in neurons}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {37},
pages = {eaec4911},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aec4911},
url = {https://doi.org/10.1126/sciadv.aec4911},
pmid = {42726865},
pmcid = {PMC13564900}
}

RIS

TY - JOUR
AU - Kelly, Tony
AU - Döngi, Michael
AU - Rodriguez-Gatica, Juan Eduardo
AU - Ofer, Netanel
AU - Wert-Carvajal, Carlos
AU - Cissewski, Niclas
AU - Barboni, Michela
AU - Bethge, Philipp
AU - Godale, Christin M
AU - Yaser, Sarah
AU - Herde, Michel K
AU - Tillmann, Jens
AU - Peter, Sabrina Ingrid
AU - Dupraz, Sebastian
AU - Koch, Henner
AU - Stein, Valentin
AU - Bradke, Frank
AU - Danzer, Steve C
AU - Tchumatchenko, Tatjana
AU - Schwarz, Martin K
AU - Kubitscheck, Ulrich
AU - Nägerl, U Valentin
AU - Beck, Heinz
TI - Dendritic shaft constrictions shape synaptic integration in neurons
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/09/11
VL - 12
IS - 37
SP - eaec4911
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aec4911
UR - https://doi.org/10.1126/sciadv.aec4911
LA - en
ER -

CSL-JSON

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