OSCR

Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions.

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, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Analysis of extracellular field potentials ↔ Code/Matlab/Fig5.m, the whole file · a weak match · score 0.72 · Fourier transform, LFP waveform, cutoff, window, wavelet, bandpass
  2. [2] § Results › Synchronous inputs: outward transmembrane currents from active dendrites contribute to positive deflection in extracellular potentials associated with gap junctional inputs ↔ Code/Matlab/Fig1.m, lines 42–104 · score 0.62 · 190–300 um, 50–100 um, proximal electrode, 190 um, gap junctions, distal
  3. [3] § Methods › Analysis of extracellular field potentials ↔ Code/Matlab/Fig1.m, lines 4–41 · score 0.57 · bandpass filtered, cutoff, window, signals, EFP, electrodes
  4. [4] § Results › Synchronous inputs: contrasting patterns of extracellular signatures associated with active dendritic chemical synapses vs. gap junctions ↔ Code/Matlab/Fig1.m, lines 42–104 · score 0.55 · 190–300 um, 50–100 um, 190 um, gap junctions, distal, traces

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

MATLAB · 258 lines · 8.6 KB · no license · 3 matches

  1. % GJLFP, eLife 2026. Richa Sirmaur and Rishikesh Narayanan.
  2. % Figure 1: waveforms (A-B),boxplots active vs passive (C-D), waveforms across entire neuron (E)
  4. %% Load dataset and bandpass filter EFP signals
  6. % loading the EFP data from all 7 arrays, all having 49 electrodes
  7. dataFiles = {
  8. 'S1_allLFP_E0-E48.xlsx'
  9. 'S2_allLFP_E0-E48.xlsx'
  10. 'S3_allLFP_E0-E48.xlsx'
  11. 'S4_allLFP_E0-E48.xlsx'
  12. 'S5_allLFP_E0-E48.xlsx'
  13. 'S6_allLFP_E0-E48.xlsx'
  14. 'S7_allLFP_E0-E48.xlsx'
  15. };
  16. dataRange = 'A1:AW80000';
  17. nFiles = numel(dataFiles); % seven
  19. % FILTER PARAMETERS ----------------
  20. fs = 40000; % sampling frequency (Hz)
  21. fc1 = 0.5; % low cutoff (Hz)
  22. fc2 = 300; % high cutoff (Hz)
  23. Wn = [fc1 fc2] / (fs/2); % cutoff
  24. n = 2; % filter order
  25. [b,a] = butter(n, Wn, 'bandpass');
  27. % LOAD + FILTER ----------------
  28. lfpData = [];
  29. for f = 1:nFiles
  30. Mat = xlsread(dataFiles{f}, dataRange); % Load dataset
  31. lfpMat = zeros(size(Mat)); % filtered matrix
  32. % Filter each electrode trace
  33. for i = 1:size(Mat,2)
  34. lfpMat(:,i) = filtfilt(b, a, Mat(:,i)); %forward-backward filtering for no time shift
  35. end
  36. lfpData{f} = lfpMat;
  37. end
  38. filteredmat = cat(2, lfpData{:}); % concatenating all arrays column-wise (7 arrays each with 49 electrodes) in one matrix
  39. dt = 1/fs;
  40. T = (0:size(Mat,1)-1)' * dt;
  41. % T = time(22001:34001); % specific plotting window
  42. %% A-B plot example waveforms
  44. % ####### A:
  46. basePath = '/chemsyn/synchro/passive/EFP'; % change to '../active/EFP' for active models
  47. % ===================== PROXIMAL (50–100 µm) =====================
  48. cd(fullfile(basePath, 'electrode_numbers_50-100'));
  50. % Load electrode indices
  51. q = []; % if already loaded and saved call the mat file: load('electrode_numbers.mat'); q = electrode_numbers (n=15)
  52. % combine proximal electrode data for traces
  53. data1 = [];
  54. for k = 1:numel(q)
  55. data1(:,k) = lfpMat1(:, q(k)); % arrays whose electrode numbers are proximal
  56. end
  57. data2 = [];
  58. for k = 1:numel(q)
  59. data2(:,k) = lfpMat2(:, q(k)); % arrays whose electrode numbers are proximal
  60. end
  61. LFP_prox = [data1, data2]; % Combine
  62. avg_prox = mean(LFP_prox, 2); % Average across proximal electrodes
  64. figure; % Plot
  65. hold on;
  66. for j = 1:15
  67. plot(T, LFP_prox(:,j), 'b', 'LineWidth', 1.5); % 'r' for active
  68. end
  70. plot(T, avg_prox, 'k', 'LineWidth', 3);
  71. title('LFPs: 50–100 µm (proximal- passive)');
  72. xlabel('Time');
  73. ylabel('LFP (uV)');
  74. hold off;
  76. % ===================== distal (190-300 µm) =====================
  77. cd(fullfile(basePath, 'electrode_numbers_190-300'));
  78. % Load electrode indices
  79. q = []; % load('electrode_numbers.mat'); q = electrode_numbers (n=21)
  80. % combine distal electrode data for traces
  81. data1 = [];
  82. for k = 1:numel(q)
  83. data1(:,k) = lfpMat6(:, q(k)); % arrays whose electrode numbers are picked as distal
  84. end
  85. data2 = [];
  86. for k = 1:numel(q)
  87. data2(:,k) = lfpMat7(:, q(k)); % arrays whose electrode numbers are picked as distal
  88. end
  89. LFP_dist = [data1, data2]; % Combine
  90. avg_dist = mean(LFP_dist, 2); % Average across distal electrodes
  92. figure; % Plot
  93. hold on;
  94. for j = 1:21
  95. plot(T, LFP_dist(:,j), 'b', 'LineWidth', 1.5); % 'r' for active
  96. end
  97. plot(T, avg_dist, 'k', 'LineWidth', 3);
  98. title('LFPs: 190-300 µm (distal- passive)'); % change to active
  99. xlabel('Time (ms)');
  100. ylabel('LFP (uV)');
  101. hold off;
  103. % ####### B:
  104. % SAME AS 'A' but for gap junctions: /gapj/synchro/passive/EFP, and /gapj/synchro/active/EFP
  106. %% Boxplot- active vs passive
  108. nBins = 10;
  109. activePath = '/sync/active/Negativeamps'; % files have negative amplitudes (negative peaks-baseline) from each electrodes divided into 10 bins based on radial distance from soma
  110. passivePath = '/sync/passive/Negativeamps';
  111. colors = [
  112. 190/255, 30/255, 45/255; % active
  113. 27/255, 117/255, 187/255 % passive
  114. ];
  115. activeColor = colors(1,:);
  116. passiveColor = colors(2,:);
  117. activeData = cell(nBins,1);
  118. passiveData = cell(nBins,1);
  120. for i = 1:nBins
  121. fname = sprintf('Bin%d.txt', i);
  122. activeData{i} = load(fullfile(activePath, fname));
  123. passiveData{i} = load(fullfile(passivePath, fname));
  124. end
  126. meanActive = cellfun(@mean, activeData);
  127. meanPassive = cellfun(@mean, passiveData);
  129. x = 1:nBins;
  130. xActive = x - 0.2;
  131. xPassive = x + 0.2;
  132. figure; hold on;
  133. boxWidth = 0.35;
  134. % boxplots:
  135. for i = 1:nBins
  137. % ---- ACTIVE ----
  138. boxplot(activeData{i}, 'Positions', xActive(i), ...
  139. 'Widths', boxWidth, ...
  140. 'Colors', activeColor, ...
  141. 'Symbol', '');
  142. h = findobj(gca,'Tag','Box');
  143. patch(get(h(1),'XData'), get(h(1),'YData'), ...
  144. activeColor, 'FaceAlpha', 0.2, ...
  145. 'EdgeColor', activeColor, 'LineWidth', 2);
  147. % ---- PASSIVE ----
  148. boxplot(passiveData{i}, 'Positions', xPassive(i), ...
  149. 'Widths', boxWidth, ...
  150. 'Colors', passiveColor, ...
  151. 'Symbol', '');
  152. h = findobj(gca,'Tag','Box');
  153. patch(get(h(1),'XData'), get(h(1),'YData'), ...
  154. passiveColor, 'FaceAlpha', 0.2, ...
  155. 'EdgeColor', passiveColor, 'LineWidth', 2);
  157. end
  158. % set(findobj(gca,'Type','line'),'LineStyle','-');
  159. xticks(linspace(0,250,6));
  160. xticklabels(string(linspace(0,250,6)));
  161. xlabel('Radial distance (µm)');
  162. ylabel('LFP');
  163. title('Active vs Passive LFP');
  164. set(gca, 'YScale', 'log');
  165. box off;
  166. hold off;
  168. % Inset: line plot:
  169. fogure; hold on;
  170. plot(x, meanActive, '-', ...
  171. 'Color', activeColor, ...
  172. 'LineWidth', 2);
  173. plot(x, meanPassive, '-', ...
  174. 'Color', passiveColor, ...
  175. 'LineWidth', 2);
  176. set(gca, 'XLim', [0 250]);
  177. box off;
  178. hold off;
  182. %% waveforms spanning the entire neuron (E)
  184. % basal waveforms from above mentioned filtered mat, load and filter EFP recorded from apical dendritic side
  185. dataFiles_apical = {
  186. 'S1_allLFP_E0-E48.xlsx'
  187. 'S2_allLFP_E0-E48.xlsx'
  188. 'S3_allLFP_E0-E48.xlsx'
  189. 'S4_allLFP_E0-E48.xlsx'
  190. 'S5_allLFP_E0-E48.xlsx'
  191. 'S6_allLFP_E0-E48.xlsx'
  192. 'S7_allLFP_E0-E48.xlsx'
  193. };
  194. dataRange = 'A1:AW80000';
  195. nFiles = numel(dataFiles); % seven
  197. lfpData_apical = [];
  198. for f = 1:nFiles
  199. Mat_apical = xlsread(dataFiles_apical{f}, dataRange); % Load dataset
  200. lfpMat = zeros(size(Mat_apical)); % filtered matrix
  201. % Filter each electrode trace
  202. for i = 1:size(Mat_apical,2)
  203. lfpMat(:,i) = filtfilt(b, a, Mat_apical(:,i)); %forward-backward filtering for no time shift
  204. end
  205. lfpData_apical{f} = lfpMat_apical;
  206. end
  207. filteredmat_apical = cat(2, lfpData_apical{:}); % concatenating all arrays column-wise (7 arrays each with 49 electrodes) in one matrix
  208. T = T(4001:48001,1);
  210. % basal:
  211. waveform_1 = FilteredMat2(4001:48001,32); waveforms(:,1) = waveform_1;
  212. waveform_2 = FilteredMat3(4001:48001,32); waveforms(:,2) = waveform_2;
  213. waveform_3 = FilteredMat3(4001:48001,39); waveforms(:,3) = waveform_3;
  214. waveform_4 = FilteredMat1(4001:48001,34); waveforms(:,4) = waveform_4;
  215. waveform_5 = FilteredMat3(4001:48001,45); waveforms(:,5) = waveform_5;
  216. waveform_6 = FilteredMat1(4001:48001,36); waveforms(:,6) = waveform_6;
  217. waveform_7 = FilteredMat6(4001:48001,47); waveforms(:,7) = waveform_7;
  218. % waveform_8 = FilteredMat2(4001:48001,4); waveforms(:,8) = waveform_8;
  219. % waveform_9 = FilteredMat3(4001:48001,1); waveforms(:,9) = waveform_9;
  220. % waveform_10 = FilteredMat6(4001:48001,7); waveforms(:,10) = waveform_10;
  222. % apical
  223. Ap_waveforms_A = zeros(length(T), num_Ap_waveforms);
  224. Ap_waveform_11 = filteredmat_apical2(4001:48001,32); Ap_waveforms_A(:,1) = Ap_waveform_11;
  225. Ap_waveform_12 = filteredmat_apical3(4001:48001,32); Ap_waveforms_A(:,2) = Ap_waveform_12;
  226. Ap_waveform_13 = filteredmat_apical3(4001:48001,39); Ap_waveforms_A(:,3) = Ap_waveform_13;
  227. Ap_waveform_14 = filteredmat_apical1(4001:48001,34); Ap_waveforms_A(:,4) = Ap_waveform_14;
  228. Ap_waveform_15 = filteredmat_apical3(4001:48001,45); Ap_waveforms_A(:,5) = Ap_waveform_15;
  229. Ap_waveform_16 = filteredmat_apical1(4001:48001,36); Ap_waveforms_A(:,6) = Ap_waveform_16;
  230. Ap_waveform_17 = filteredmat_apical6(4001:48001,47); Ap_waveforms_A(:,7) = Ap_waveform_17;
  231. % Ap_waveform_18 = filteredmat_apical2(4001:48001,4); Ap_waveforms_A(:,8) = Ap_waveform_18;
  232. % Ap_waveform_19 = filteredmat_apical3(4001:48001,1); Ap_waveforms_A(:,9) = Ap_waveform_19;
  233. % Ap_waveform_20 = filteredmat_apical6(4001:48001,7); Ap_waveforms_A(:,10) = Ap_waveform_20;
  236. figure;
  237. num_waveforms = 7; % Number of waveforms in each set (taking 7 for plotting)
  238. total_waveforms = 14; % Total number
  239. for i = 1:total_waveforms
  240. subplot(total_waveforms, 1, i);
  241. if i <= num_waveforms
  242. % Plot the first 7 waveforms from waveforms (basal)
  243. plot(T, waveforms(:,i));
  244. else
  245. % Plot the next 7 waveforms from Ap_waveforms (apical)
  246. plot(T, Ap_waveforms_(:,i - num_waveforms));
  247. end
  248. xlabel('Time');
  249. ylabel('Amplitude');
  250. % title(['Waveform ', num2str(i)]); % Uncomment this line if you want to add titles
  251. % xlim([100 1200]);
  252. % ylim([-100 100]);
  253. box off;
  254. end
  256. % do the same for gap junctional EFPs for right side waveforms.

Fig1.m, no license · at the source

Overview

Authors: Richa Sirmaur1, Rishikesh Narayanan1
ORCID iDs: Richa Sirmaur, Rishikesh Narayanan
  1. Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
Institutions: Indian Institute of Science Bangalore (India)
Journal: eLife, volume 14, article RP103046
Dates: published online 30 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.103046 · PMID 42528398 · PMCID PMC13423354 · OpenAlex W4407969355
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: None
MeSH: Dendrites*, Gap Junctions*, Synapses*, Action Potentials, Animals, Local Field Potential Measurement (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: DBT/Wellcome Trust India Alliance (Senior fellowship IA/S/16/2/502727, IA/S/16/2/502727); Ministry of Education, India (Scholarship and research funds)
Citations: not cited yet (Europe PMC); 129 references in the paper
Research resources: Igor Pro RRID:SCR_000325, MATLAB RRID:SCR_001622, R Project for Statistical Computing RRID:SCR_001905, NEURON RRID:SCR_005393, Python Programming Language RRID:SCR_008394, LFPy RRID:SCR_014805, Jupyter Notebook RRID:SCR_018315

Abstract

Local field potentials (LFPs) are compound signals that represent the dynamic flow of information across the brain, which have been historically associated with chemical synaptic inputs. How do gap junctional inputs onto active compartments shape LFPs? We developed a methodology to record extracellular potentials associated with different patterns of gap junctional inputs onto conductance-based models. We found that synchronous inputs through chemical synapses yielded a negative deflection in proximal extracellular electrodes whereas those onto gap junctions manifested a positive deflection. Importantly, we observed extracellular dipoles only when inputs arrived through chemical synapses but not with gap junctions. Remarkably, hyperpolarization-activation cyclic nucleotide-gated channels, which typically conduct inward currents, mediated outward currents triggered by the fast voltage transition caused by synchronous inputs. With rhythmic inputs at different frequencies arriving through gap junctions, we found strong suppression of LFP power at higher frequencies as well as frequency-dependent differences in the spike phase associated with the LFP when compared to respective chemical synaptic counterparts. All observed differences in LFP were mediated by the relative dominance of synaptic currents vs. voltage-driven transmembrane currents with chemical synapses vs. gap junctions, respectively. Our analyses unveil a hitherto unknown role for active dendritic gap junctions in shaping extracellular 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.

Zenodo 21312084

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: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (50 files), Signal Processing Toolbox (7 files), LFPy (6 files), Matplotlib (6 files), NumPy (6 files), Statistics and Machine Learning Toolbox (2 files), Wavelet Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
58 files
At the source: doi.org/10.5281/zenodo.21312084

supp:PMC13423354/elife-103046-code1.zip

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: NEURON (44), C (36), MATLAB (8), Python (6), C++ (2)
Size: 96 files, 96 scripts
Software Heritage: not checked
Found in: the supplementary material
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (50 files), Signal Processing Toolbox (7 files), LFPy (6 files), Matplotlib (6 files), NumPy (6 files), Statistics and Machine Learning Toolbox (2 files), Wavelet Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
96 files
At the source: pmc-oa-opendata.s3.amazonaws.com/PMC13423354.1/elife-103046-code1.zip

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;
  • 154 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

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. All source codes for simulation and analyses are publicly available at https://doi.org/10.5281/zenodo.21312084.

The following dataset was generated:

Sirmaur R, Narayanan R. 2026. Codes. Zenodo.

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, 6 MeSH terms, 2 funders, 127 references, 7 RRIDs.

Cite

This paper

Sirmaur, R., & Narayanan, R. (2026). Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions. eLife, 14, RP103046. https://doi.org/10.7554/elife.103046

BibTeX

@article{sirmaur2026dichotomy,
author = {Sirmaur, Richa and Narayanan, Rishikesh},
title = {{Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP103046},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.103046},
url = {https://doi.org/10.7554/elife.103046},
pmid = {42528398},
pmcid = {PMC13423354}
}

RIS

TY - JOUR
AU - Sirmaur, Richa
AU - Narayanan, Rishikesh
TI - Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/07/30
VL - 14
SP - RP103046
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.103046
UR - https://doi.org/10.7554/elife.103046
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.103046",
"type": "article-journal",
"title": "Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions",
"container-title": "eLife",
"author": [
{
"family": "Sirmaur",
"given": "Richa"
},
{
"family": "Narayanan",
"given": "Rishikesh"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP103046",
"DOI": "10.7554/elife.103046",
"PMID": "42528398",
"PMCID": "PMC13423354",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.103046",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
30
]
]
}
}

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:10.1038/s41398-026-04018-1
Loss of connexin 36 elicits abnormalities in thalamocortical network activity relevant to neuropsychiatric disorders.
Journal: Translational psychiatry
In common: 8 references
[2] 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, Matplotlib, NumPy, cellular / molecular, 5 references
[3] doi:10.1371/journal.pcbi.1014283 [code]
Spatial richness of neural magnetic fields.
Journal: PLoS computational biology
In common: LFPy, NEURON, Matplotlib, 1 other tool, 3 references
[4] doi:10.7554/elife.108352 [code]
Analysis of dendritic input currents during place field dynamics.
Journal: eLife
In common: NEURON, Matplotlib, NumPy, 4 references
[5] doi:10.1002/advs.202519893 [code]
NeuroSuite for Long-Term Functional and Structural Studies of Air-Liquid Interface Cerebral Organoids.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: Wavelet Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 2 other tools, extracellular electrophysiology (units, LFP), 2 references
[6] doi:10.1038/s41586-026-10331-y [code]
Active dissociation of intracortical spiking and high gamma activity.
Journal: Nature
In common: Matplotlib, NumPy, extracellular electrophysiology (units, LFP), 5 references
[7] doi:10.1162/imag.a.1229 [code]
40 Hz audiovisual stimulation improves sustained attention and related brain oscillations.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Wavelet Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 2 other tools, 2 references
[8] doi:10.1016/j.celrep.2026.117646 [code]
Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology.
Journal: Cell reports
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, Matplotlib, 1 other tool, 3 references
[9] doi:10.1371/journal.pcbi.1014304 [code]
Linking reduced prefrontal microcircuit inhibition in schizophrenia to EEG biomarkers in silico.
Journal: PLoS computational biology
In common: LFPy, NEURON, Matplotlib, 1 other tool, 1 reference
[10] doi:10.7554/elife.100642 [code]
Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury.
Journal: eLife
In common: Wavelet Toolbox, Signal Processing Toolbox, 3 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.