Spatial continuity of neurons explains non-random network architecture.

Code ↔ Paper

9 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 9 matches

[1] § STAR★Methods › Method details › Selecting a subset of connections to analyze in the MICrONS data ↔ src/Match and compare to microns.ipynb, lines 340–427 · score 0.69 · L5ET, L5NP, L5a, L5b, cells, MICrONS
[2] § STAR★Methods › Method details › Reference for graph structure - MICrONS ↔ src/pnagm/util.py, lines 54–113 · score 0.66 · classification_system, excitatory_neuron, MICrONS
[3] § STAR★Methods › Method details › Stochastic geometric spread graph model (SGSG) ↔ src/pnagm/util.py, lines 115–148 · score 0.65 · stochastic spread graph, dimensional space, random geometric graph, nodes, model
[4] § Results › Central hypothesis: Neuron physicality shapes network structure ↔ src/Nearest neighbor 1d prob increase.ipynb, lines 1–51 · score 0.59 · post synaptic neuron, connection demonstrates, future potential connections, probability function, oriented, pre
[5] § STAR★Methods › Method details › Reference for graph structure - MICrONS ↔ src/Nearest neighbor 2d prob increase.ipynb, lines 86–108 · score 0.58 · classification_system, excitatory_neuron, classes
[6] § Results › A simplified model of the axon physicality effect leads to non-random micro-structure ↔ src/pnagm/util.py, lines 54–113 · score 0.55 · soma locations, excitatory neurons, MICrONS
[7] § STAR★Methods › Method details › Graph control models ↔ src/connalysis/randomization/randomization.py, lines 537–571 · score 0.54 · Bishuffled model, bidirectional connections, match, graph, network
[8] § STAR★Methods › Method details › Stochastic geometric spread graph model (SGSG) ↔ src/connalysis/randomization/randomization.py, lines 957–1106 · score 0.51 · stochastic spread model, candidate, outgoing, graphs, edge, node
[9] § Results › Testing the hypothesis in excitatory connectomes ↔ src/Nearest neighbor 1d prob increase.ipynb, lines 1–51 · score 0.50 · MICrONS, modeled network, post synaptic, synaptic neurons, potential connections, pre

Paper

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The authors' code

Python · 149 lines · 5.6 KB · GPL-3.0 · 3 matches

import numpy
import h5py
import pandas
import os
# For the random or non-random generation of neuron locations in space
def make_points(cfg):
"""
Generate a random point cloude from a parameterization dict.
"""
n_nrn = cfg["n_nrn"]
tgt_sz = cfg["tgt_sz"]
if not hasattr(tgt_sz, "__iter__"):
tgt_sz = [tgt_sz, tgt_sz, tgt_sz]
tgt_sz = numpy.array(tgt_sz).reshape((1, -1))
pts = numpy.random.rand(n_nrn, 3) * tgt_sz - tgt_sz/2
return pts
def no_categorical_dtypes(N):
"""
Utility function: Turns categorical node properties into non-categorical ones. This is required
for some analyses for technical reasons.
"""
import pandas
for col in N._vertex_properties.columns:
if isinstance(N._vertex_properties[col].dtype, pandas.CategoricalDtype):
N._vertex_properties[col] = N._vertex_properties[col].astype(str)
def load_all_instances_from_file(fn):
"""
Utility function to load all ConnectivityMatrix objects from an .h5 file. This function is not very
clever and strongly assumes that any group found in the .h5 file contains a ConnectivityMatrix, without
additional checks.
"""
import conntility
grp_tuples = []
with h5py.File(fn, "r") as h5:
for prefix in h5.keys():
if isinstance(h5[prefix], h5py.Group):
for grp_name in h5[prefix].keys():
if isinstance(h5[prefix][grp_name], h5py.Group):
grp_tuples.append((prefix, grp_name))
matrices = [conntility.ConnectivityMatrix.from_h5(fn, group_name=grp_name,
prefix=prefix)
for prefix, grp_name in grp_tuples]
matrices = conntility.ConnectivityGroup(
pandas.DataFrame({"instance": range(len(matrices))}), matrices
)
return matrices
def points_from_microns(cfg, return_additional_controls=False):
"""
Generate a point cloud by looking up soma locations from a reference connectome. The reference could
be the MICrONS EM connectome, hence the name. But in principle any source can be used, as long as it
provides soma locations.
Args:
cfg: dict configuring the process, i.e., which connectome to load and which subvolume to select.
"""
import conntility
if "root" in cfg:
fn = cfg["root"] + "/" + cfg["fn"]
else:
fn = cfg["fn"]
try:
N = conntility.ConnectivityMatrix.from_h5(fn, "condensed")
except:
N = conntility.ConnectivityMatrix.from_h5(fn)
no_categorical_dtypes(N)
sz = cfg["tgt_sz"]
cols = ["x_nm", "y_nm", "z_nm"]
for _col in cols:
if _col in N._vertex_properties.columns:
N._vertex_properties[_col[0]] = N._vertex_properties[_col] / 1000.0
tl_col_dict = {
"ss_flat_x": "x", "ss_flat_y": "z", "depth": "y"
}
for _col_in, _col_out in tl_col_dict.items():
if _col_in in N._vertex_properties.columns:
N._vertex_properties[_col_out] = N._vertex_properties[_col_in] + numpy.random.rand(len(N)) * 1E-9
cols = ["x", "y", "z"]
center = N.vertices[cols].mean()
for k, v in cfg.get("filters", {"classification_system": "excitatory_neuron"}).items():
if isinstance(v, list):
N = N.index(k).isin(v)
else:
N = N.index(k).eq(v)
for _col in cols:
_col_o = "o_" + _col
if _col_o in cfg:
_o = cfg[_col_o]
N = N.index(_col).le(center[_col] + _o + sz/2).index(_col).ge(center[_col] + _o - sz/2)
print(len(N))
pts = N.vertices[cols].values
if return_additional_controls:
additional_controls = {}
for add_ctrl_name, add_ctrl in cfg.get("additional_controls", {}).items():
ctrl_fn = add_ctrl["fn"]
if "root" in add_ctrl.keys():
ctrl_fn = os.path.join(add_ctrl["root"], ctrl_fn)
additional_controls[add_ctrl_name] = load_all_instances_from_file(ctrl_fn)
return pts, N, additional_controls
return pts, N
def create_neighbor_spread_graph(pts, cfg, reference=None):
"""
Utility function that combines generation of a random geometric graph and generation of a stochastic
spread graph on the first graph.
Args:
pts (numpy.array; m x n): Locations of m nodes in n-dimensional space.
cfg (dict): Configures generation of the two graphs. For details, see README and the docstrings of
pnagm.nngraph.cand2_point_nn_matrix and instance.build_instance.
reference (optional; conntility.ConnectivityMatrix): If "per_class_bias" is used for the generation of
the random geometric graph, then a reference connectome is required. See README and
nngraph.generate_custom_weights_by_node_class for details.
"""
from . import nngraph, instance
w_out_use = None
w_in_use = None
if "per_class_bias" in cfg:
assert reference is not None, "When using per class bias, must provide reference ConnectivityMatrix"
if "outgoing" in cfg["per_class_bias"]:
prop = cfg["per_class_bias"]["outgoing"]
w_out_use = nngraph.generate_custom_weights_by_node_class(reference, prop, 1)
if "incoming" in cfg["per_class_bias"]:
prop = cfg["per_class_bias"]["incoming"]
w_in_use = nngraph.generate_custom_weights_by_node_class(reference, prop, 0)
M = nngraph.cand2_point_nn_matrix(pts,
custom_w_out=w_out_use, custom_w_in=w_in_use,
**cfg["nngraph"]).astype(bool).astype(float)
mdl_instance, a, b = instance.build_instance(pts, M, **cfg["instance"])
return mdl_instance, M

util.py at commit 0902376, under GPL-3.0 · at the source

Overview

Authors: Michael W Reimann¹, Daniela Egas Santander^1,2,3, Lida Kanari⁴, Natalí Barros-Zulaica¹

ORCID iDs: Michael W Reimann

Institutions: Open Brain Institute (Switzerland); Center for Systems Biology Dresden (Germany); Max Planck Institute of Molecular Cell Biology and Genetics (Germany); University of Oxford (United Kingdom)

Journal: iScience, volume 29, issue 6, article 116144

Dates: received 13 October 2025; accepted 12 May 2026; published online 1 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.116144 · PMID 42291180 · PMCID PMC13253143 · OpenAlex W7163002029

Open access: gold, a free copy (OpenAlex)

Status: code verified

Methods: Statistics, Connectivity, Graphs, Machine learning

Keywords: biological sciences

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

Funding: UK Research and Innovation; UK Research and Innovation Medical Research Council (MR/Z504804/1)

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Neuronal networks are characterized by complex and functionally relevant connectivity motifs. We developed an intuitive explanation for its emergence. While a class of neurons on average innervates its entire surroundings, each individual neuron can only cover a small part of the space. That region is different for each neuron but not completely random, as it is physically constrained by the spatial continuity of the axon. This hypothesis was successfully tested against a morphologically detailed model and an electron-microscopic reconstruction of cortical connectivity. We distilled it into a stochastic algorithm that generates networks, which accurately match the reference data. Our work bridges previous efforts to capture network complexity with top-down or bottom-up methods, that is, by adding complexity constraints to simple stochastic models or by predicting synapses from neuron appositions. It may improve the understanding of the impact of neuron malformations and the functional role of non-random network structure in simplified models.

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 9 matches between paragraphs and lines of code.

Zenodo 20084936

License: apgl-v3

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the resources table

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (16 files), SciPy (16 files), pandas (14 files), Matplotlib (6 files), NetworkX (4 files), seaborn (1 file), statsmodels (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

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35 files

build.py, Python, 94 lines
data_analysis/SSCx_modelling/model_building_batch.sh, Shell, 7 lines
data_analysis/SSCx_modelling/model_building_order2.sh, Shell, 20 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local.py, Python, 51 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local_per_pathway.py, Python, 91 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local_per_pathway.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange.py, Python, 52 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange_per_pathway.py, Python, 91 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange_per_pathway.sh, Shell, 15 lines
data_analysis/analysis_configs/simple_example.py, Python, 31 lines
src/connalysis/__init__.py, Python, 12 lines
src/connalysis/modelling/__init__.py, Python, 32 lines
src/connalysis/modelling/modelling.py, Python, 1,476 lines
src/connalysis/network/__init__.py, Python, 16 lines
src/connalysis/network/classic.py, Python, 906 lines
src/connalysis/network/local.py, Python, 357 lines
src/connalysis/network/stats.py, Python, 383 lines
src/connalysis/network/topology.py, Python, 1,733 lines
src/connalysis/randomization/__init__.py, Python, 12 lines
src/connalysis/randomization/rand_utils.py, Python, 343 lines
src/connalysis/randomization/randomization.py, Python, 1,106 lines
tests/test_imports.py, Python, 22 lines
tests/test_network_topology.py, Python, 13 lines
tutorials/TDA_unweighted_networks.ipynb, Jupyter, 282 lines
tutorials/counting_triads.ipynb, Jupyter, 39 lines
tutorials/modelling.ipynb, Jupyter, 287 lines
tutorials/plot_simplices.ipynb, Jupyter, 34 lines
tutorials/plot_triads.ipynb, Jupyter, 87 lines
tutorials/randomization.ipynb, Jupyter, 136 lines
tutorials/run_batch_modelling.py, Python, 52 lines
tutorials/run_batch_modelling.sh, Shell, 22 lines
LICENCE, License, 661 lines
README.md, Text, 91 lines

At the source:

Zenodo 10059227

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the resources table

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (39 files), NumPy (38 files), SciPy (19 files), Matplotlib (12 files), h5py (4 files), rpy2 (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

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56 files

conntility/__init__.py, Python, 4 lines
conntility/analysis/__init__.py, Python, 21 lines
conntility/analysis/analysis.py, Python, 204 lines
conntility/analysis/analysis_decorators.py, Python, 286 lines
conntility/analysis/clustering.py, Python, 71 lines
conntility/analysis/library/__init__.py, Python, 2 lines
conntility/analysis/library/diffusion_mapping.py, Python, 66 lines
conntility/circuit_models/__init__.py, Python, 7 lines
conntility/circuit_models/connection_matrix.py, Python, 547 lines
conntility/circuit_models/input_spikes.py, Python, 109 lines
conntility/circuit_models/neuron_groups/__init__.py, Python, 7 lines
conntility/circuit_models/neuron_groups/defaults.py, Python, 13 lines
conntility/circuit_models/neuron_groups/extra_properties.py, Python, 102 lines
conntility/circuit_models/neuron_groups/from_atlas.py, Python, 57 lines
conntility/circuit_models/neuron_groups/grouping_config.py, Python, 167 lines
conntility/circuit_models/neuron_groups/loader.py, Python, 131 lines
conntility/circuit_models/neuron_groups/make_groups.py, Python, 191 lines
conntility/circuit_models/neuron_groups/sonata_extensions.py, Python, 67 lines
conntility/circuit_models/neuron_groups/tessellate.py, Python, 533 lines
conntility/circuit_models/sonata_helpers.py, Python, 95 lines
conntility/connectivity.py, Python, 1,868 lines
conntility/flatmapping/__init__.py, Python, 4 lines
conntility/flatmapping/_supersample_utility.py, Python, 139 lines
conntility/flatmapping/flatmap_utility.py, Python, 116 lines
conntility/flatmapping/supersampling.py, Python, 177 lines
conntility/flatmapping/wm_recipe_utility.py, Python, 97 lines
conntility/io/__init__.py, Python, 2 lines
conntility/io/logging.py, Python, 36 lines
conntility/io/sparse_matrices.py, Python, 96 lines
conntility/io/synapse_report.py, Python, 126 lines
conntility/multi_scale.py, Python, 262 lines
conntility/plugins.py, Python, 105 lines
conntility/randomization/__init__.py, Python, 1 line
conntility/subcellular/__init__.py, Python, 2 lines
conntility/subcellular/neuron_morphology_path_distance.py, Python, 301 lines
examples/C elegans - a non-sonata-based example.ipynb, Jupyter, 393 lines
examples/Correlation against distance.ipynb, Jupyter, 98 lines
examples/Edge participation in MICrONS.ipynb, Jupyter, 396 lines
examples/Example 3 - Generating control subsamples and neighborhoods.ipynb, Jupyter, 213 lines
examples/Example 4 - Loading atlas data.ipynb, Jupyter, 184 lines
examples/Example 5 - Connectivity at reduced resolution.ipynb, Jupyter, 233 lines
examples/Example 6 - Plastic matrices.ipynb, Jupyter, 159 lines
examples/Examples 1 and 2 - Analyzing pathways and controls.ipynb, Jupyter, 492 lines
examples/Fly connectome - a non-sonata based example.ipynb, Jupyter, 151 lines
examples/Fly larva - a non-sonata-based example.ipynb, Jupyter, 149 lines
examples/MICrONS_connectome_inhibitory_innervation.ipynb, Jupyter, 166 lines
examples/Slicing and sampling.ipynb, Jupyter, 280 lines
tests/data/random_er.py, Python, 11 lines
tests/data/random_simplex_counts.py, Python, 15 lines
tests/test_analysis.py, Python, 20 lines
tests/test_analysis_decorator.py, Python, 83 lines
tests/test_connectivity_matrix.py, Python, 184 lines
tests/test_neuron_groups.py, Python, 37 lines
tests/utils.py, Python, 26 lines
LICENSE, License, 201 lines
README.md, Text, 196 lines

At the source:

Zenodo 20055048

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the resources table

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (14 files), pandas (14 files), SciPy (14 files), Matplotlib (11 files), h5py (3 files), NetworkX (3 files)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

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18 files

src/Characterization.ipynb, Jupyter, 533 lines
src/Characterization_recursive.ipynb, Jupyter, 386 lines
src/Conn prob correlations between spatial bins.ipynb, Jupyter, 636 lines
src/Draw explanatory cartoons.ipynb, Jupyter, 154 lines
src/Fit other models to microns.ipynb, Jupyter, 480 lines
src/Long range extension.ipynb, Jupyter, 342 lines
src/Match and compare to microns.ipynb, Jupyter, 517 lines
src/Microns mean l5 connection prob.ipynb, Jupyter, 44 lines
src/Microns nearest neighbor effect.ipynb, Jupyter, 174 lines
src/Nearest neighbor 1d prob increase.ipynb, Jupyter, 479 lines
src/Nearest neighbor 2d prob increase.ipynb, Jupyter, 550 lines
src/pnagm/__init__.py, Python, 1 line
src/pnagm/instance.py, Python, 146 lines
src/pnagm/nngraph.py, Python, 241 lines
src/pnagm/test.py, Python, 398 lines
src/pnagm/util.py, Python, 149 lines
LICENSE, License, 674 lines
README.md, Text, 112 lines

At the source:

bluebrain/connectomeutilities

License: Apache-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: f0186b2cda2abc4e4604f26303ee1f2dc7cef21f, 26 February 2025

Languages: Python (42), Jupyter (12)

Size: 81 files, 54 scripts

Software Heritage: archived

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Holds: README, license file, environment (pyproject.toml, requirements.txt), tests, continuous integration, 12 notebooks

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56 files

conntility/__init__.py, Python, 4 lines
conntility/analysis/__init__.py, Python, 21 lines
conntility/analysis/analysis.py, Python, 204 lines
conntility/analysis/analysis_decorators.py, Python, 286 lines
conntility/analysis/clustering.py, Python, 71 lines
conntility/analysis/library/__init__.py, Python, 2 lines
conntility/analysis/library/diffusion_mapping.py, Python, 66 lines
conntility/circuit_models/__init__.py, Python, 7 lines
conntility/circuit_models/connection_matrix.py, Python, 547 lines
conntility/circuit_models/input_spikes.py, Python, 109 lines
conntility/circuit_models/neuron_groups/__init__.py, Python, 7 lines
conntility/circuit_models/neuron_groups/defaults.py, Python, 13 lines
conntility/circuit_models/neuron_groups/extra_properties.py, Python, 102 lines
conntility/circuit_models/neuron_groups/from_atlas.py, Python, 57 lines
conntility/circuit_models/neuron_groups/grouping_config.py, Python, 167 lines
conntility/circuit_models/neuron_groups/loader.py, Python, 131 lines
conntility/circuit_models/neuron_groups/make_groups.py, Python, 191 lines
conntility/circuit_models/neuron_groups/sonata_extensions.py, Python, 67 lines
conntility/circuit_models/neuron_groups/tessellate.py, Python, 533 lines
conntility/circuit_models/sonata_helpers.py, Python, 95 lines
conntility/connectivity.py, Python, 1,927 lines
conntility/flatmapping/__init__.py, Python, 4 lines
conntility/flatmapping/_supersample_utility.py, Python, 139 lines
conntility/flatmapping/flatmap_utility.py, Python, 117 lines
conntility/flatmapping/supersampling.py, Python, 177 lines
conntility/flatmapping/wm_recipe_utility.py, Python, 97 lines
conntility/io/__init__.py, Python, 2 lines
conntility/io/logging.py, Python, 36 lines
conntility/io/sparse_matrices.py, Python, 96 lines
conntility/io/synapse_report.py, Python, 126 lines
conntility/multi_scale.py, Python, 262 lines
conntility/plugins.py, Python, 105 lines
conntility/randomization/__init__.py, Python, 1 line
conntility/subcellular/__init__.py, Python, 2 lines
conntility/subcellular/neuron_morphology_path_distance.py, Python, 301 lines
examples/C elegans - a non-sonata-based example.ipynb, Jupyter, 393 lines
examples/Correlation against distance.ipynb, Jupyter, 98 lines
examples/Edge participation in MICrONS.ipynb, Jupyter, 396 lines
examples/Example 3 - Generating control subsamples and neighborhoods.ipynb, Jupyter, 213 lines
examples/Example 4 - Loading atlas data.ipynb, Jupyter, 184 lines
examples/Example 5 - Connectivity at reduced resolution.ipynb, Jupyter, 233 lines
examples/Example 6 - Plastic matrices.ipynb, Jupyter, 159 lines
examples/Examples 1 and 2 - Analyzing pathways and controls.ipynb, Jupyter, 492 lines
examples/Fly connectome - a non-sonata based example.ipynb, Jupyter, 151 lines
examples/Fly larva - a non-sonata-based example.ipynb, Jupyter, 149 lines
examples/MICrONS_connectome_inhibitory_innervation.ipynb, Jupyter, 166 lines
examples/Slicing and sampling.ipynb, Jupyter, 280 lines
tests/data/random_er.py, Python, 11 lines
tests/data/random_simplex_counts.py, Python, 15 lines
tests/test_analysis.py, Python, 20 lines
tests/test_analysis_decorator.py, Python, 83 lines
tests/test_connectivity_matrix.py, Python, 184 lines
tests/test_neuron_groups.py, Python, 37 lines
tests/utils.py, Python, 26 lines
LICENSE, License, 201 lines
README.md, Text, 200 lines

At the source: github.com/BlueBrain/ConnectomeUtilities/tree/v0.4.6

mwolfr/local_connectivity_model

License: GPL-3.0

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Languages: Jupyter (11), Python (5)

Size: 28 files, 16 scripts

Software Heritage: not archived

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18 files

src/Characterization.ipynb, Jupyter, 533 lines
src/Characterization_recursive.ipynb, Jupyter, 386 lines
src/Conn prob correlations between spatial bins.ipynb, Jupyter, 636 lines
src/Draw explanatory cartoons.ipynb, Jupyter, 154 lines
src/Fit other models to microns.ipynb, Jupyter, 480 lines
src/Long range extension.ipynb, Jupyter, 342 lines
src/Match and compare to microns.ipynb, Jupyter, 517 lines, 1 match
src/Microns mean l5 connection prob.ipynb, Jupyter, 44 lines
src/Microns nearest neighbor effect.ipynb, Jupyter, 174 lines
src/Nearest neighbor 1d prob increase.ipynb, Jupyter, 479 lines, 2 matches
src/Nearest neighbor 2d prob increase.ipynb, Jupyter, 550 lines, 1 match
src/pnagm/__init__.py, Python, 1 line
src/pnagm/instance.py, Python, 146 lines
src/pnagm/nngraph.py, Python, 241 lines
src/pnagm/test.py, Python, 398 lines
src/pnagm/util.py, Python, 149 lines, 3 matches
LICENSE, License, 674 lines
README.md, Text, 114 lines

At the source: github.com/MWolfR/local_connectivity_model/tree/v1.0.0

openbraininstitute/connectome-analysis

License: AGPL-3.0

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Languages: Python (20), Shell (7), Jupyter (6)

Size: 72 files, 33 scripts

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Tools: NumPy (16 files), SciPy (16 files), pandas (14 files), Matplotlib (6 files), NetworkX (4 files), seaborn (1 file), statsmodels (1 file)

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35 files

build.py, Python, 94 lines
data_analysis/SSCx_modelling/model_building_batch.sh, Shell, 7 lines
data_analysis/SSCx_modelling/model_building_order2.sh, Shell, 20 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local.py, Python, 51 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local_per_pathway.py, Python, 91 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_local_per_pathway.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange.py, Python, 52 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange.sh, Shell, 15 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange_per_pathway.py, Python, 91 lines
data_analysis/SSCx_modelling/run_SSCx_model_building_order2_midrange_per_pathway.sh, Shell, 15 lines
data_analysis/analysis_configs/simple_example.py, Python, 31 lines
src/connalysis/__init__.py, Python, 12 lines
src/connalysis/modelling/__init__.py, Python, 32 lines
src/connalysis/modelling/modelling.py, Python, 1,475 lines
src/connalysis/network/__init__.py, Python, 16 lines
src/connalysis/network/classic.py, Python, 906 lines
src/connalysis/network/local.py, Python, 357 lines
src/connalysis/network/stats.py, Python, 383 lines
src/connalysis/network/topology.py, Python, 1,733 lines
src/connalysis/randomization/__init__.py, Python, 12 lines
src/connalysis/randomization/rand_utils.py, Python, 343 lines
src/connalysis/randomization/randomization.py, Python, 1,106 lines, 2 matches
tests/test_imports.py, Python, 22 lines
tests/test_network_topology.py, Python, 13 lines
tutorials/TDA_unweighted_networks.ipynb, Jupyter, 282 lines
tutorials/counting_triads.ipynb, Jupyter, 39 lines
tutorials/modelling.ipynb, Jupyter, 287 lines
tutorials/plot_simplices.ipynb, Jupyter, 34 lines
tutorials/plot_triads.ipynb, Jupyter, 87 lines
tutorials/randomization.ipynb, Jupyter, 136 lines
tutorials/run_batch_modelling.py, Python, 52 lines
tutorials/run_batch_modelling.sh, Shell, 22 lines
LICENCE, License, 661 lines
README.md, Text, 91 lines

At the source: github.com/openbraininstitute/connectome-analysis/tree/v1.1.0

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:

6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
206 scripts, each with its path and the digest of its content;
9 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 and code availability

This work uses connectomics data in a custom, hdf5-based format for the modeling of connectivity and to enable comparisons to reference data. All such connectomes have been deposited on Zenodo. Accession numbers (DOIs) are listed in the key resources table.

Original code executing and analyzing the model, and generating the figures has been archived on Zenodo. DOIs are listed in the key resources table. The code depends on two custom software packages developed by the authors, “connectome-analysis” and “connectome-utilities,” that are listed on the pypi tracker and archived on Zenodo. Accession numbers (DOIs) are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper are available from the lead contact upon request.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 1 keyword, 2 funders, 78 references.

Cite

This paper

Reimann, M. W., Egas Santander, D., Kanari, L., & Barros-Zulaica, N. (2026). Spatial continuity of neurons explains non-random network architecture. iScience, 29(6), 116144. https://doi.org/10.1016/j.isci.2026.116144

BibTeX

@article{reimann2026spatial, author = {Reimann, Michael W and Egas Santander, Daniela and Kanari, Lida and Barros-Zulaica, Natalí}, title = {{Spatial continuity of neurons explains non-random network architecture}}, journal = {iScience}, year = {2026}, month = jun, volume = {29}, number = {6}, pages = {116144}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116144}, url = {https://doi.org/10.1016/j.isci.2026.116144}, pmid = {42291180}, pmcid = {PMC13253143} }

RIS

TY - JOUR AU - Reimann, Michael W AU - Egas Santander, Daniela AU - Kanari, Lida AU - Barros-Zulaica, Natalí TI - Spatial continuity of neurons explains non-random network architecture T2 - iScience J2 - iScience PY - 2026 DA - 2026/06/01 VL - 29 IS - 6 SP - 116144 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116144 UR - https://doi.org/10.1016/j.isci.2026.116144 LA - en ER -

CSL-JSON

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Spatial continuity of neurons explains non-random network architecture.

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Zenodo 20084936

Zenodo 10059227

Zenodo 20055048

bluebrain/connectomeutilities

mwolfr/local_connectivity_model

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