OSCR

Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes.

Code ↔ Paper

8 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 8 matches
  1. [1] § Material and Methods › Network model ↔ figure_generator/supp_connectivity.py, lines 62–133 · score 0.62 · pre synaptic, connection strength, post synaptic, ratio
  2. [2] § Results › Effect of local change in synaptic connectivity on sequence dynamics ↔ figure_generator/figure2.py, lines 48–181 · score 0.57 · modulation strength, random neurons, Patch locations, contour, duration, Figure 2
  3. [3] § Material and Methods › Max-tree algorithm ↔ figure_generator/figure2a.py, lines 197–281 · score 0.56 · merge tree, semi transmissive, nodes, threshold, pathway, sequence
  4. [4] § Results › Semi-transmissive neurons ↔ figure_generator/figure2a.py, lines 197–281 · score 0.56 · Semi transmissive, merge tree, Sequence landscape, threshold, pathways, baseline
  5. [5] § Results › Semi-transmissive neurons ↔ transmission_network.py, lines 167–188 · score 0.53 · sigmoidal transfer function, rate model, transmission, neurons
  6. [6] § Results › Semi-transmissive neurons ↔ lib/brian.py, lines 156–173 · score 0.52 · sigmoidal transfer function, rate model, synaptic, neurons
  7. [7] § Results › Mid in-degree regions alter the dynamics of branching and merging of sequences › Gate ↔ figure_generator/cooperativity.py, lines 200–300 · score 0.51 · active neurons, sequences crossed, detection spot, cooperation, space, branch
  8. [8] § Results › Mid in-degree regions alter the dynamics of branching and merging of sequences › Gate ↔ figure_generator/figure5.py, lines 173–255 · score 0.51 · active neurons, sequences crossed, detection spot, cooperation, B1, B2

Paper

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

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The authors' code

Python · 291 lines · 11 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Summary:
  5. """
  6. #===============================================================================
  7. # PROGRAM METADATA
  8. #===============================================================================
  9. __author__ = 'Hauke Wernecke'
  10. __contact__ = '[email hidden]'
  11. __version__ = '0.1'
  13. #===============================================================================
  14. # IMPORT STATEMENTS
  15. #===============================================================================
  16. from cflogger import logger
  18. import numpy as np
  19. import matplotlib.pyplot as plt
  20. from matplotlib import rcParams
  22. from skimage.morphology import max_tree
  23. import networkx as nx
  24. from dataclasses import dataclass
  25. import matplotlib.gridspec as gridspec
  28. from params import config
  29. import lib.universal as UNI
  30. import lib.pickler as PIC
  31. from plot.lib.frame import create_image, create_images_on_axes
  32. from plot.lib.basic import add_colorbar, plot_patch_from_tag, add_colorbar_from_im, add_topright_spines, remove_spines_and_ticks
  33. from plot.constants import cm, KTH_PINK
  34. from plot.sequences import _get_sequence_landscape
  35. from lib.connectivitymatrix import ConnectivityMatrix
  37. from figure_generator.figure1 import indegree_low, indegree_high
  39. from tree import grow_forest, hierarchy_pos_custom_levels
  40. #===============================================================================
  41. # CONSTANTS
  42. #===============================================================================
  43. # rcParams["font.size"] = 8
  44. # rcParams["figure.figsize"] = (17.6*cm, 6*cm)
  45. figsize = (17.6*cm, 10*cm)
  46. # rcParams["legend.fontsize"] = 7
  47. # rcParams["legend.framealpha"] = 1
  48. # rcParams["axes.labelpad"] = 2
  50. filename = "transmissive_neurons"
  52. example_seed = 3
  53. seeds = np.arange(8)
  54. #===============================================================================
  55. # MAIN METHOD
  56. #===============================================================================
  57. def main():
  58. fig = plt.figure(figsize=figsize)
  59. # gs = fig.add_gridspec(nrows=4, ncols=4, width_ratios=(1.2, 1, .4, 1), height_ratios=(1, 1.3, 0.8, 3))
  60. # gs = fig.add_gridspec(nrows=2, ncols=4, width_ratios=(1.2, 1, .4, 1), height_ratios=(1, 1.3))
  61. gs = fig.add_gridspec(nrows=1, ncols=4, width_ratios=(1.8, 1, .6, 1))
  62. fig.subplots_adjust(
  63. left=0.0,
  64. right=0.99,
  65. bottom=0.54,
  66. top=0.9,
  67. wspace=0.0,
  68. hspace=0.50,
  69. )
  71. # cmap = plt.cm.hot_r
  72. from plot.sequences import truncate_colormap
  73. cmap = truncate_colormap(plt.cm.hot_r, 0, .9)
  74. ax = fig.add_subplot(gs[0, 0], projection= "3d") ####################################################################
  76. tag = config.baseline_tag(seed=example_seed)
  77. spikes, labels = PIC.load_spike_train(tag, config)
  78. seq_count = _get_sequence_landscape(spikes, labels, config.rows)
  79. S = np.arange(config.rows)
  80. X, Y = np.meshgrid(S, S)
  81. ax.plot_surface(X, Y, seq_count.T, edgecolor="grey", lw=0.1, rstride=2, cstride=2,
  82. alpha=0.4, cmap=cmap)
  84. zticks = (0, 20)
  85. xyticks = (10, 50, 90)
  86. ax.contour(X, Y, seq_count.T, zdir="z", offset=-40, cmap=cmap)
  87. ax.set(
  88. xlim=(0, config.rows), ylim=(0, config.rows), zlim=(-40, seq_count.max()),
  89. xticks=xyticks, yticks=xyticks, zticks=zticks,
  90. xlabel="X", ylabel="Y", zlabel='Seq. count', title="Sequence Landscape")
  92. elev = 20 # defines the angle of the camera location above the x-y plane.
  93. azim = -105 # rotates the camera about the vertical axis, with a positive angle corresponding to a right-handed rotation.
  94. roll = 0 # rotates the camera about the viewing axis.
  95. ax.view_init(elev, azim, roll)
  100. from skimage.measure import find_contours
  101. seq_counts = np.zeros((seeds.size, config.rows, config.rows), dtype=int)
  102. mask = np.zeros((config.rows, config.rows), dtype=bool)
  103. for s, seed in enumerate(seeds):
  104. tag = config.baseline_tag(seed=seed)
  105. spikes, labels = PIC.load_spike_train(tag, config)
  106. seq_count = _get_sequence_landscape(spikes, labels, config.rows)
  107. seq_counts[s] = seq_count
  108. force_forest = False
  109. fname = f"bridge_{s}"
  110. try:
  111. if force_forest:
  112. raise FileNotFoundError
  113. bridge_neurons = PIC.load(fname)
  114. except FileNotFoundError:
  115. forest, merges, bridge_neurons = grow_forest(seq_count)
  116. PIC.save(fname, bridge_neurons)
  117. mask[bridge_neurons[:, 1], bridge_neurons[:, 0]] = True
  119. ax = fig.add_subplot(gs[0, 1]) ####################################################################
  120. im = create_image(seq_counts.mean(axis=0).T, cmap=cmap, axis=ax)
  121. ax.set(
  122. xticks=xyticks, yticks=xyticks,
  123. xlabel="X", ylabel="Y", title="Sequence Counts")
  124. add_topright_spines(ax)
  125. cbar = add_colorbar_from_im(ax, im)
  126. cbar.set_ticks(np.linspace(0, 30, 4, dtype=int))
  127. cbar.set_label("Seq. count", rotation=270, labelpad=10)
  129. contours = find_contours(mask, 0.5)
  130. contour_kwargs = {"color": "lime", "linewidth": 1}
  131. for c in contours:
  132. ax.plot(c[:, 1], c[:, 0], **contour_kwargs)
  135. fname = f"bridge_{example_seed}"
  137. # bridge_neurons = PIC.load(fname)
  138. conn = ConnectivityMatrix(config)
  139. indegree, _ = conn.degree(conn._EE)
  140. indegree = indegree * config.synapse.weight
  142. tag = config.baseline_tag(seed=example_seed)
  143. avgRate = PIC.load_average_rate(tag, sub_directory=config.sub_dir, config=config)
  145. gs_hist = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec = gs[0, 3], hspace=0.)
  146. # ax = fig.add_subplot(gs[1, 3])
  147. ax = fig.add_subplot(gs_hist[1])
  148. ax.set(xlabel="In-degree", ylabel="Avg. rate", yticks=(0, 0.1, 0.2, 0.3), xlim=(indegree_low, indegree_high))
  149. scatter_kwargs = {"marker": ".", "s": 8, "edgecolor": 'none'}
  150. ax.scatter(indegree.flatten(), avgRate, **scatter_kwargs)
  151. ax.scatter(indegree[mask].flatten(), avgRate[mask.flatten()], c=contour_kwargs["color"], **scatter_kwargs)
  154. # ax_hist = fig.add_subplot(gs[0, 3])
  155. ax_hist = fig.add_subplot(gs_hist[0])
  156. ax_hist.set(title="Semi-Transmissive\nNeurons", xlim=(indegree_low, indegree_high))
  157. remove_spines_and_ticks(ax_hist)
  160. hist_kwargs = {"bottom": 0.2, "range": (indegree_low, indegree_high), "bins": 15, "rwidth": 0.8}
  161. H, edges, _ = ax_hist.hist(indegree.flatten(), **hist_kwargs)
  162. bridge_degrees = indegree[mask].flatten()
  163. ax_hist.hist(bridge_degrees, **hist_kwargs, color=contour_kwargs["color"])
  165. separator = np.linspace(indegree.min(), indegree.max(), 5+1)
  166. from figure_generator.figure2 import map_indegree_to_color
  167. for sep in separator[1:-1]:
  168. color = map_indegree_to_color(sep, indegree_low, indegree_high)
  169. ax_hist.axvline(sep, ls="--", c=color, zorder=12)
  170. ax.axvline(sep, ls="--", c=color, zorder=12)
  174. gs_bottom = fig.add_gridspec(nrows=1, ncols=2, width_ratios=(1.2, 1), left=.2, top=0.36, bottom=0.1, wspace=0.6, right=0.8)
  176. merge_filename = "merge_counter"
  177. force_merge = False
  178. merge_counter = []
  179. try:
  180. if force_merge:
  181. raise FileNotFoundError
  182. merge_counter = PIC.load(merge_filename)
  183. except FileNotFoundError:
  184. pass
  185. if not merge_counter:
  186. merge_counter = []
  187. for base in np.arange(23, 23+20+1):
  188. config.landscape.params["base"] = base
  189. tag = config.baseline_tag(seed=0)
  190. spikes, labels = PIC.load_spike_train(tag, config)
  191. seq_count = _get_sequence_landscape(spikes, labels, config.rows)
  192. forest, merges, bridge_neurons = grow_forest(seq_count)
  193. merge_counter.append(len(merges))
  194. PIC.save(merge_filename, merge_counter)
  197. # gs_bottom = gridspec.GridSpecFromSubplotSpec(nrows=1, ncols=2, subplot_spec = gs[1, :], wspace=.2)
  198. ax = fig.add_subplot(gs_bottom[0, 0])
  199. ax.set(
  200. yticks=(0, 10, 20, 30, 40),
  201. ylabel="Threshold", title="Merge Tree")
  203. config.landscape.params["base"] = 23
  204. tag = config.baseline_tag(seed=0)
  205. spikes, labels = PIC.load_spike_train(tag, config)
  206. seq_count = _get_sequence_landscape(spikes, labels, config.rows)
  207. forest, merges, _ = grow_forest(seq_count)
  209. G = nx.DiGraph()
  210. G.add_nodes_from([t._id for t in forest.trees])
  211. levels = {tree._id: list(tree.levels.keys())[0] for tree in forest.trees}
  213. extra_nodes = []
  214. edges = []
  215. merged_replacements = {}
  216. for merge in merges:
  217. level, (root_node, branch_node), intersection = merge
  218. merge_node = f"{root_node} ({level})"
  219. if merge_node not in extra_nodes:
  220. extra_nodes.append(merge_node)
  222. if root_node in merged_replacements.keys():
  223. if merge_node != merged_replacements[root_node]:
  224. edges.append((merge_node, merged_replacements[root_node]))
  225. else:
  226. if merge_node != root_node:
  227. edges.append((merge_node, root_node))
  229. if branch_node in merged_replacements.keys():
  230. if merge_node != merged_replacements[branch_node]:
  231. edges.append((merge_node, merged_replacements[branch_node]))
  232. else:
  233. if merge_node != branch_node:
  234. edges.append((merge_node, branch_node))
  236. merged_replacements[root_node] = merge_node
  237. # edges.append((root_node, merge_node))
  238. # edges.append((branch_node, merge_node))
  239. levels[merge_node] = level
  240. G.add_nodes_from(extra_nodes)
  241. G.add_edges_from(edges)
  244. nx.set_node_attributes(G, levels, "level")
  246. leafs = forest.get_leafs()
  247. pos = {}
  248. xshift = 0
  249. for l, leaf in enumerate(leafs):
  250. pos_tmp = hierarchy_pos_custom_levels(G, merged_replacements.get(leaf._id, leaf._id), x_start=xshift)
  251. pos.update(pos_tmp)
  252. xshift = np.asarray(list(pos_tmp.values()), dtype=float)[:, 0].max() + 2
  254. nx.draw(
  255. G,
  256. pos,
  257. with_labels=False,
  258. node_size=50,
  259. ax=ax,
  260. arrowsize = 6,
  261. arrowstyle = "<|-",
  262. node_color = nx.get_node_attributes(G, "level").values(),
  263. # node_color = "tab:brown",
  264. node_shape = "v",
  265. cmap = cmap,
  266. edge_color = "k",
  267. edgecolors = "k"
  268. )
  269. ax.set_axis_on()
  270. ax.yaxis.set_visible(True)
  271. ax.spines["left"].set_visible(True)
  272. ax.tick_params(axis="y", which="both", left=True, labelleft=True)
  274. ax = fig.add_subplot(gs_bottom[0, 1])
  275. ax.set(
  276. yticks=(0, 2, 4, 6),
  277. ylabel="Occurences", xlabel="Number of merges", title="Semi-Transmissive Pathways")
  278. ax.hist(merge_counter, bins=np.arange(0, np.asarray(merge_counter).max()+1, 1), rwidth=0.8, color=KTH_PINK)
  281. PIC.save_figure(filename, fig)
  282. #===============================================================================
  283. # METHODS
  284. #===============================================================================
  288. #===============================================================================
  289. if __name__ == '__main__':
  290. main()
  291. plt.show()

figure2a.py at commit 537e7ed, no license · at the source

Overview

Authors: Hauke O. Wernecke1,2, Andrew B. Lehr3, Arvind Kumar1,2
ORCID iDs: Hauke O. Wernecke, Andrew B. Lehr, Arvind Kumar
  1. Department of Computational Science and Technology, School of Electrical Engineering and Computer Science and Digital Futures, KTH Royal Institute of Technology, Stockholm 11428, Sweden
  2. Science for Life Laboratory, Solna 171 65, Sweden
  3. Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen 37073, Germany
Institutions: Science for Life Laboratory (Sweden); KTH Royal Institute of Technology (Sweden); Universitätsmedizin Göttingen (Germany); University of Göttingen (Germany)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 22, article e1506252026
Dates: received 2 August 2025; accepted 29 March 2026; published online 3 June 2026; in print 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.1506-25.2026 · PMID 42086319 · PMCID PMC13233934 · OpenAlex W4412626095
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.24.666534
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Single-unit activity, calcium imaging
Keywords: computational neuroscience, dynamical networks, neuromodulation, neuroscience
MeSH: Models, Neurological*, Nerve Net*, Neurons*, Synapses*, Action Potentials, Animals (* major topic)
Journal subjects: Systems/Circuits
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Strategic Research Area Neuroscience; Vetenskapsrådet (VR) (2018-03118)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

The neural basis of behavior is believed to consist of sequential patterns of neural activity in the relevant brain regions. Behavioral flexibility also requires neural circuit mechanisms that support dynamic control of sequential activity. However, mechanisms to control and reconfigure sequential activity have received little attention. Here, we show that recurrently connected networks with heterogeneous connectivity and a smooth spatial in-degree landscape (which may arise due to asymmetric neuron morphologies) provide a robust mechanism to evoke and control sequential activity. By modulating the synaptic strength of only a few neurons in local neighborhoods, we uncovered high-impact locations that can start, stop, extend, gate, and redirect sequences. Interestingly, high-impact locations coincide with mid in-degree regions. We demonstrate that these motifs can flexibly reconfigure sequential activity, and hence, provide a framework for fast and flexible computations on behavioral timescales, while the individual parts of the pathways remain rigid and reliable.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Jiggaboy/local_modulation

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 537e7ed3567e5d01f4c2700a3908823283900d69, 13 February 2026
Languages: Python (89), Jupyter (2)
Size: 123 files, 91 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, tests, 2 notebooks
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Tools: NumPy (65 files), Matplotlib (52 files), pandas (7 files), Brian 2 (4 files), scikit-learn (4 files), NetworkX (3 files), scikit-image (3 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
92 files
At the source: github.com/Jiggaboy/local_modulation.git

Code Availability

The code for reproducing the simulations and the analyses can be found at GitHub https://github.com/Jiggaboy/local_modulation.git.

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 91 scripts, each with its path and the digest of its content;
  • 8 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.

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 2, 28 September 2026

  • Authors: added Andrew B. Lehr (0000-0002-1838-1847); Arvind Kumar (0000-0002-8044-9195); removed Andrew B. Lehr; Arvind Kumar

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 6 MeSH terms, 2 funders, 44 references.

Cite

This paper

Wernecke, H. O., Lehr, A. B., & Kumar, A. (2026). Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(22), e1506252026. https://doi.org/10.1523/jneurosci.1506-25.2026

BibTeX

@article{wernecke2026controlling,
author = {Wernecke, Hauke O. and Lehr, Andrew B. and Kumar, Arvind},
title = {{Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = jun,
volume = {46},
number = {22},
pages = {e1506252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.1506-25.2026},
url = {https://doi.org/10.1523/jneurosci.1506-25.2026},
pmid = {42086319},
pmcid = {PMC13233934}
}

RIS

TY - JOUR
AU - Wernecke, Hauke O.
AU - Lehr, Andrew B.
AU - Kumar, Arvind
TI - Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/06/03
VL - 46
IS - 22
SP - e1506252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.1506-25.2026
UR - https://doi.org/10.1523/jneurosci.1506-25.2026
LA - en
ER -

CSL-JSON

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"id": "10.1523/jneurosci.1506-25.2026",
"type": "article-journal",
"title": "Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes",
"container-title": "The Journal of neuroscience : the official journal of the Society for Neuroscience",
"author": [
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"family": "Wernecke",
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{
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"container-title-short": "J Neurosci",
"volume": "46",
"issue": "22",
"page": "e1506252026",
"DOI": "10.1523/jneurosci.1506-25.2026",
"PMID": "42086319",
"PMCID": "PMC13233934",
"ISSN": "0270-6474",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/jneurosci.1506-25.2026",
"language": "en",
"issued": {
"date-parts": [
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2026,
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In common: scikit-learn, SciPy, Matplotlib, 1 other tool, 3 references

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