OSCR

Analysis of dendritic input currents during place field dynamics.

Code ↔ Paper

7 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 7 matches
  1. [1] § Results › CSBs in the CA1 pyramidal neuron model ↔ simulator/ModelSimulator.py, lines 13–99 · score 0.57 · Synapse locations, excitatory synapses, inhibitory synapses, membrane potential, weights, clusters
  2. [2] § Results › CSBs in the CA1 pyramidal neuron model ↔ simulator/ModelSimulator.py, lines 32–77 · score 0.56 · Synapse locations, excitatory synapses, inhibitory synapses, weights, clusters, synaptic
  3. [3] § Methods › Extended currentscape calculation ↔ currentscape_calculator/partitioning_order.py, lines 54–76 · score 0.55 · directed graph, depth, DFS, subgraphs, edge, reverses
  4. [4] § Results › The extended currentscape method ↔ currentscape_calculator/partitioning_order.py, lines 54–76 · score 0.55 · leaf nodes, target node, algorithm, edges, reversed, tree
  5. [5] § Methods › Biophysical models › Simplified neuron model (Figure 2) ↔ simulator/model/ca1_model.py, lines 79–120 · score 0.55 · membrane capacitance, resistivity, cm, Passive, soma, model
  6. [6] § Methods › Biophysical models › Simplified neuron model (Figure 2) ↔ simulator/model/ca1_model.py, lines 79–120 · score 0.54 · delayed rectifier, sodium, potassium, propagation, density, soma
  7. [7] § Methods › Preprocessing › Preprocessing membrane and synaptic currents ↔ currentscape_calculator/partitioning_algorithm.py, lines 12–100 · score 0.53 · partitioning algorithm, dendritic branch, recalculated, reindexed, axial, soma

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 102 lines · 4.3 KB · MIT · 2 matches

  1. import networkx as nx
  2. import pandas as pd
  5. def create_directed_graph(iax: pd.DataFrame, tp: int) -> nx.DiGraph:
  6. """
  7. Creates a directed graph based on the axial current value (iax) at a specified time point (tp).
  9. Parameters:
  10. iax (df): A pandas DataFrame with axial current data. It must include a column for the specified time point (`tp`)
  11. and index columns representing 'ref' and 'par' segments.
  12. tp (int): The time point for which to construct the graph using axial current values.
  14. Returns:
  15. DiGraph: A directed graph where edges are added based on the sign of the axial current values.
  16. - If `iax` is positive, the edge direction is `par -> ref`.
  17. - If `iax` is negative, the edge direction is `ref -> par`.
  18. """
  19. df_iax_tp = iax[tp]
  20. df_iax_tp = df_iax_tp.reset_index()
  21. df_iax_tp.rename(columns={tp: "iax"}, inplace=True) # has three columns: ref, par, iax
  23. # Create directed graph (add edges to the graph based on the sign of iax)
  24. dg = nx.DiGraph()
  25. for index, row in df_iax_tp.iterrows():
  26. if row['iax'] >= 0:
  27. dg.add_edge(row['par'], row['ref'], iax=row['iax']) # par -> ref if 'iax_timepoint' is positive
  28. elif row['iax'] < 0:
  29. dg.add_edge(row['ref'], row['par'], iax=row['iax']) # ref -> par if 'iax_timepoint' is negative
  30. return dg
  33. def get_partitioning_order(dg: nx.DiGraph, target: str, direction: str) -> list[tuple[str, str]]:
  34. """
  35. Determines the partitioning order of nodes in a directed graph.
  37. Parameters:
  38. dg (DiGraph): The directed graph.
  39. target (str): The node from which the traversal starts.
  40. direction (str): The traversal direction. Options are:
  41. - "out": Outward traversal from the target node.
  42. - "in": Inward traversal towards the target node.
  44. Returns:
  45. list[tuple[str, str]]: A list of node pairs representing the traversal order.
  46. """
  47. traversal_methods = {
  48. "out": get_traversal_order_out,
  49. "in": get_traversal_order_in
  50. }
  51. return traversal_methods[direction](dg, target)
  54. def get_traversal_order_out(dg: nx.DiGraph, target: str) -> list[tuple[str, str]]:
  55. """
  56. Computes the outward traversal order from a target node in a directed graph.
  58. Parameters:
  59. dg (DiGraph): The directed graph.
  60. target (str): The node from which the outward traversal starts.
  62. Returns:
  63. list[tuple[str, str]]: A list of node pairs representing the traversal order,
  64. starting from the leaf nodes.
  65. """
  66. # Find subgraph using depth first search algorithm and copy iax values of edges
  67. dg_dfs_out = nx.dfs_tree(dg, source=target)
  68. for u, v in dg_dfs_out.edges():
  69. if dg.has_edge(u, v):
  70. dg_dfs_out[u][v]['iax'] = dg[u][v]['iax']
  72. # Extract traversal order starting from the leaf nodes
  73. edges_visited_out = list(nx.edge_dfs(dg_dfs_out, target))
  74. node_pairs_out = [(v, u) for (u, v) in edges_visited_out] # switch nodes of each edge
  75. node_pairs_out.reverse() # reverse node pairs order (to start from the leaf nodes)
  76. return node_pairs_out
  79. def get_traversal_order_in(dg: nx.DiGraph, target: str) -> list[tuple[str, str]]:
  80. """
  81. Computes the inward traversal order towards a target node in a directed graph.
  83. Parameters:
  84. dg (nx.DiGraph): The directed graph.
  85. target (str): The node towards which the inward traversal is computed.
  87. Returns:
  88. list[tuple[str, str]]: A list of node pairs representing the traversal order,
  89. starting from the leaf nodes.
  90. """
  91. dg_reversed = nx.reverse(dg, copy=True)
  92. # Find subgraph using depth first search algorithm and copy iax values of edges
  93. dg_dfs_in = nx.dfs_tree(dg_reversed, source=target)
  94. for u, v in dg_dfs_in.edges():
  95. if dg.has_edge(v, u):
  96. dg_dfs_in[u][v]['iax'] = dg[v][u]['iax']
  98. # Extract traversal order starting from the leaf nodes
  99. edges_visited_in = list(nx.edge_dfs(dg_dfs_in, target))
  100. node_pairs_in = [(v, u) for (u, v) in edges_visited_in] # switch nodes of each edge
  101. node_pairs_in.reverse() # reverse node pairs order (to start from the leaf nodes)
  102. return node_pairs_in

partitioning_order.py at commit aa3400b, under MIT · at the source

Overview

Authors: Bence Fogel1, Balazs B Ujfalussy1
ORCID iDs: Bence Fogel, Balazs B Ujfalussy
  1. Biological Computation Research Group, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
Institutions: HUN-REN Institute of Experimental Medicine (Hungary)
Journal: eLife, volume 14, article RP108352
Dates: published online 3 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108352 · PMID 42397126 · PMCID PMC13331480 · OpenAlex W4415928299
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: systems (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Preprocessing, Single-unit activity, calcium imaging
Keywords: None
MeSH: Dendrites*, Hippocampus*, Neurons*, Place Cells*, Action Potentials, Animals, Membrane Potentials, Models, Neurological (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research, Development and Innovation Office of Hungary (FK-125324)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Neuronal activity is driven by the complex interplay between various membrane currents, often located in distinct domains of the spatially extended dendritic tree. How the effect of these currents propagates to the soma and contributes to neuronal output under in vivo conditions is not fully understood. Here, we develop a new method to measure and visualize the contributions of individual membrane currents to the somatic response in spatially extended biophysical model neurons. Our approach relies on the iterative decomposition of the axial current flowing between neighbouring compartments in proportion to the underlying membrane currents measured in the model. We apply this method to visualize the inputs driving hippocampal place cell activity. Our method provides a compact and intuitive description of the various dendritic events underlying subthreshold activity, spiking, or burst firing. By contrasting the dendritic input currents preceding spiking and bursting, we demonstrate that both could occur at highly variable input levels to proximal dendrites (basal and oblique), and that strong distal inputs facilitate, rather than control, the generation of complex spike bursts. Our method opens a novel window onto single-neuron computations that will help to design better models and to interpret the results of in vivo imaging experiments.

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 7 matches between paragraphs and lines of code.

bencefogel/currentscapes-invivo-demo

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: aa3400b156126e149bdf2a4ec3c039e9a3801980, 30 June 2026
Languages: Python (25), NEURON (9)
Size: 76 files, 34 scripts
Software Heritage: archived
Found in: the references
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NEURON (18 files), pandas (16 files), NumPy (15 files), NetworkX (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: archive.softwareheritage.org/swh:1:dir:4a87d874692ed114b788a5e0f5dccf87cc2c7c63;origin=https://github.com/bencefogel/currentscapes-invivo-demo;visit=swh:1:snp:2134842b42ccfb0867d740b020225c4f8eb6222c;anchor=swh:1:rev:aa3400b156126e149bdf2a4ec3c039e9a3801980

bencefogel/currentscapes-simple-model-examples

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 926d5555bc5f575005fd1c12e90663a93591f8e0, 30 June 2026
Languages: Python (23), NEURON (9), Jupyter (3)
Size: 78 files, 35 scripts
Software Heritage: archived
Found in: the text, “Biophysical models”
Holds: README, license file, environment (requirements.txt), 3 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NEURON (21 files), pandas (19 files), NumPy (18 files), NetworkX (5 files), Matplotlib (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
37 files
At the source: github.com/bencefogel/currentscapes-simple-model-examples

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

Tracing map

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;
  • 69 scripts, each with its path and the digest of its content;
  • 7 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

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

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code is available at Github.

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, pages, dates, 2 authors, 1 keyword, 8 MeSH terms, 1 funder, 89 references.

Cite

This paper

Fogel, B., & Ujfalussy, B. B. (2026). Analysis of dendritic input currents during place field dynamics. eLife, 14, RP108352. https://doi.org/10.7554/elife.108352

BibTeX

@article{fogel2026analysis,
author = {Fogel, Bence and Ujfalussy, Balazs B},
title = {{Analysis of dendritic input currents during place field dynamics}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP108352},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108352},
url = {https://doi.org/10.7554/elife.108352},
pmid = {42397126},
pmcid = {PMC13331480}
}

RIS

TY - JOUR
AU - Fogel, Bence
AU - Ujfalussy, Balazs B
TI - Analysis of dendritic input currents during place field dynamics
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/07/03
VL - 14
SP - RP108352
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108352
UR - https://doi.org/10.7554/elife.108352
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.108352",
"type": "article-journal",
"title": "Analysis of dendritic input currents during place field dynamics",
"container-title": "eLife",
"author": [
{
"family": "Fogel",
"given": "Bence"
},
{
"family": "Ujfalussy",
"given": "Balazs B"
}
],
"container-title-short": "Elife",
"volume": "14",
"page": "RP108352",
"DOI": "10.7554/elife.108352",
"PMID": "42397126",
"PMCID": "PMC13331480",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.108352",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
3
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.celrep.2026.117793 [code]
Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons.
Journal: Cell reports
In common: NEURON, pandas, Matplotlib, 1 other tool, 20 references
[2] doi:10.1523/jneurosci.1540-25.2026 [code]
Dendritic Inhibition Terminates Plateau Potentials in CA1 Pyramidal Neurons.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: 17 references
[3] doi:10.7554/elife.89629 [code]
Active dendrites enable robust spiking computations despite timing jitter.
Journal: eLife
In common: NEURON, pandas, Matplotlib, 1 other tool, 9 references
[4] doi:10.1038/s41467-026-74834-y [code]
Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.
Journal: Nature communications
In common: systems, 11 references
[5] doi: [code]
Going deeper with morphologically detailed neural networks by simulation-based gradient propagation
Journal: Frontiers in computational neuroscience
In common: NEURON, Matplotlib, NumPy, 6 references
[6] doi:10.1016/j.isci.2026.117010 [code]
Deep learning-assisted mapping of dendritic spines using sequential 2D two-photon calcium imaging.
Journal: iScience
In common: pandas, Matplotlib, NumPy, 7 references
[7] doi:10.1038/s41467-026-71503-y [code]
Rapid formation of non-spatial hippocampal representations consistent with behavioral timescale synaptic plasticity is modulated by entorhinal input.
Journal: Nature communications
In common: 8 references
[8] doi:10.1126/sciadv.adz4123 [code]
Highly attenuated dendritic propagation of isolated synaptic potentials in vivo.
Journal: Science advances
In common: 8 references
[9] doi:10.1126/sciadv.aec3961 [code]
Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.
Journal: Science advances
In common: NEURON, pandas, Matplotlib, 1 other tool, 5 references
[10] doi:10.1016/j.celrep.2026.117388 [code]
An unsuspected physiological role for mGluRIII glutamate receptors in hippocampal area CA1.
Journal: Cell reports
In common: NEURON, pandas, Matplotlib, 1 other tool, 5 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.