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Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Extracellular recording analysis ↔ plotNWBdata.m, lines 1–124 · score 0.63 · spike width, fast spiking, Putative, PSTHs, Extracellular, ALM
  2. [2] § Methods › Extracellular recording analysis ↔ plotNWBdata.m, lines 127–216 · score 0.63 · spike rate, smoothed, filter, causal, PSTHs, lick
  3. [3] § Results › CaMKII activity in ALM is necessary to learn new lick timing ↔ plotNWBdata.m, lines 1–124 · score 0.54 · delay duration, trial onset, cue onset, rewarded, ALM, lick

Paper

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The authors' code

MATLAB · 233 lines · 6.1 KB · no license · 3 matches

  1. function [] = plotNWBdata()
  2. % code to plot data in https://dandiarchive.org/dandiset/001848
  3. % modified the original code to plot NWB data @ https://neurodatawithoutborders.github.io/matnwb/tutorials/html/basicUsage.html
  4. % modified by Hidehiko 7/5/2026
  8. % add matnwb to your path
  9. addpath('matnwb-main');
  12. nwb = nwbRead('MI91_03032022_ALM.nwb');
  13. plot_cell_id = 6;
  16. %% read nwb file
  17. unit_names = keys(nwb.analysis);
  19. unit_ids = nwb.units.id.data.load(); % array of unit ids represented within this
  20. % Initialize trials & times Map containers indexed by unit_ids
  21. unit_trials = containers.Map('KeyType',class(unit_ids),'ValueType','any');
  22. unit_times = containers.Map('KeyType',class(unit_ids),'ValueType','any');
  23. last_idx = 0;
  24. for i = 1:length(unit_ids)
  25. unit_id = unit_ids(i);
  27. row = nwb.units.getRow(unit_id, 'useId', true, 'columns', {'spike_times', 'trialsID'});
  28. unit_trials(unit_id) = row.trialsID{1};
  29. unit_times(unit_id) = row.spike_times{1};
  30. end
  32. sorted_ids = sort(unit_ids);
  33. Photostim = struct(...
  34. 'ind', true,... % mask into xs and ys for this photostim
  35. 'name', 'none',...
  36. 'stim_duration', 0,... % in seconds after the onset of normal go cue
  37. 'stim_onset', 0); % in seconds after the onset of normal go cue
  41. % Initialize Map container of plotting data for each unit, stored as structure
  42. Unit = containers.Map('KeyType',class(unit_ids),'ValueType','any');
  43. unit_struct = struct(...
  44. 'id', [],...
  45. 'xs', [],...
  46. 'ys', [],...
  47. 'xlim', [-Inf Inf],...
  48. 'trialID',[],...
  49. 'trialTypes', 0,...
  50. 'spikeWidth',[],...
  51. 'cellTypes',[],...
  52. 'ontology',[],...
  53. 'photostim', Photostim); % can have multiple photostim
  57. % read data from indv units
  58. for unit_id = unit_ids'
  60. unit_trial_id = unit_trials(unit_id);
  62. % extract good trials to find trial range
  63. trial = nwb.intervals_trials.getRow(unit_trial_id, 'useId', true,...
  64. 'columns', {'CueOnset','GoodTrials'});
  66. unit_good_trials = logical(trial.GoodTrials) & ~isnan(trial.CueOnset);
  67. unit_trial_id = unit_trial_id(unit_good_trials);
  69. unit_spike_time = unit_times(unit_id);
  70. unit_spike_time = unit_spike_time(unit_good_trials) - trial.CueOnset(unit_good_trials); % algin to cue
  73. % count number of trials per condition
  74. % we need to do this as there could be trial w.o. spikes
  75. first_trial = min(unit_trial_id);
  76. last_trial = max(unit_trial_id);
  77. trialIDs = first_trial:last_trial;
  79. trial_in_range = nwb.intervals_trials.getRow(first_trial:last_trial, 'useId', true,...
  80. 'columns', {'CueOnset', 'DelayDuration', 'FirstLick', 'GoodTrials',...
  81. 'Unrewarded','Rewarded','NoLick','NoCue','start_time','ActualTrialOnsetTime'});
  84. % spike width
  85. SpikeWidth = nwb.general_extracellular_ephys_electrodes.getRow(1, 'useId', true,...
  86. 'columns', {'spike_width'});
  87. % note SpikeWidth>0.5 is regular spiking cells, <0.35 is fast spiking
  88. % (FS) cells.
  90. % putative cell type
  91. CellTypes = nwb.general_extracellular_ephys_electrodes.getRow(1, 'useId', true,...
  92. 'columns', {'cell_type'});
  96. % summarize spike info for plotting
  98. xs = unit_spike_time;
  99. ys = unit_trial_id;
  101. curr_unit = unit_struct;
  103. curr_unit.xs = xs;
  104. curr_unit.ys = ys;
  105. curr_unit.trialID = trialIDs;
  106. curr_unit.trialTypes = trial_in_range;
  107. curr_unit.spikeWidth = SpikeWidth;
  108. curr_unit.cellTypes = CellTypes;
  112. Unit(unit_id) = curr_unit;
  114. end
  117. %plot PSTH
  118. plot_PSTH(Unit(plot_cell_id))
  120. end
  124. %% PSTH helper function
  127. function plot_PSTH(Unit)
  130. time_bin = 0.001; % time bin for PSTH (s)
  131. T_axis = -6:time_bin:6;
  132. smooth_bin = 50; % 50ms box car smoothing
  133. v1 = ones(smooth_bin,1)/smooth_bin;
  135. spk = Unit.xs;
  136. trials = Unit.ys;
  137. trialID = Unit.trialID;
  138. trialTypes = Unit.trialTypes;
  139. numTrial = numel(trialID);
  141. PSTH = nan(numTrial,numel(T_axis));
  142. lick_time = trialTypes{:,3}; % lick time
  143. delay_dur = trialTypes{:,2}; % delay duration
  144. no_cue_trials = trialTypes{:,8}; % no cue trial or not
  145. start_time = trialTypes{:,9}; % trial start time (s)
  146. %note that this is not the actual trial start time. Data was converted to
  147. %trial based strcture (-7 to 7 sec from trial onset) in our pipeline.
  148. % Pseudo trial onset time (20s interval) was added for NWB.
  149. % actual trial onset time in sec is in ('ActualTrialOnsetTime').
  152. spkTrialAligned = [];trialsSpk=[];
  153. for i = 1:numTrial
  154. spk_mask = trials==trialID(i);
  155. spk_in_trial = spk(spk_mask)-start_time(i);
  156. spkTrialAligned = [spkTrialAligned;spk_in_trial];
  157. trialsSpk = [trialsSpk;ones(size(spk_in_trial))*i];
  159. spk_in_trial_aligned = spk_in_trial;
  160. counts = hist(spk_in_trial_aligned,T_axis);
  161. mean_spike_rate = counts/time_bin;
  162. PSTH_tmp = conv(mean_spike_rate,v1,'full');
  163. smoothedPSTH = PSTH_tmp(1:numel(mean_spike_rate)); % causal filtering
  164. PSTH(i,:) = smoothedPSTH;
  165. end
  168. % pool PSTH per lick time
  169. LT_ranges = [0.15 0.3;0.3 0.45;0.45 0.6;0.6 0.75;0.75 0.9;0.9 1.05;1.05 1.20;1.20 1.35];
  170. num_LT_range = size(LT_ranges,1);
  172. meanPSTH = nan(num_LT_range,numel(T_axis));
  174. for lt = 1:num_LT_range
  175. trMask = lick_time>=LT_ranges(lt,1) & lick_time<LT_ranges(lt,2) &~no_cue_trials;
  177. if sum(trMask)>=30
  178. meanPSTH(lt,:) = mean(PSTH(trMask,:));
  179. end
  181. end
  183. lineColor = turbo(num_LT_range);
  186. %% plot spike raster & PSTH
  187. figure;set(gcf,'Color','w','Position',[110 182 450 700])
  188. subplot(2,1,1);hold on
  189. plot(spkTrialAligned,trialsSpk,'k.')
  190. plot(lick_time,1:numTrial,'m*')
  191. plot(delay_dur,1:numTrial,'g')
  192. xlim([-1 2])
  193. ylim([0.5 numTrial+0.5])
  194. xline(0,'k:')
  195. set(gca,'tickdir','out','box','off')
  196. xlabel('Time from cue (s)')
  197. ylabel('Trials')
  198. title('Spike raster')
  200. subplot(2,1,2);hold on
  201. for lt = 1:num_LT_range
  202. plot(T_axis,meanPSTH(lt,:),'Color',lineColor(lt,:))
  203. xline(mean(LT_ranges(lt,:)),':','Color',lineColor(lt,:))
  204. end
  205. xlim([-1 2])
  206. ylims = ylim();
  207. yMax = ylims(2)*1.1;
  208. ylim([0 yMax]);
  209. xline(0,'k:')
  210. set(gca,'tickdir','out','box','off')
  211. xlabel('Time from cue (s)')
  212. ylabel('Spikes per s')
  213. title('PSTH')
  216. end

plotNWBdata.m at commit b9a84be, no license · at the source

Overview

Authors: Shouvik Majumder1,2,3, Koichi Hirokawa1,2,3, Zidan Yang1,2,3, Anant Jain1, Ronald Paletzki4, Charles R. Gerfen4, Lorenzo Fontolan5,6, Sandro Romani6, Ryohei Yasuda1, Hidehiko K. Inagaki1
ORCID iDs: Shouvik Majumder, Koichi Hirokawa, Zidan Yang, Charles R. Gerfen, Lorenzo Fontolan, Sandro Romani, Ryohei Yasuda, Hidehiko K. Inagaki
  1. Max Planck Florida Institute for Neuroscience,Jupiter, FL USA
  2. Florida Atlantic University,Boca Raton, FL USA
  3. IMPRS for Synapses and Circuits, Jupiter, FL USA
  4. National Institute of Mental Health,Bethesda, MD USA
  5. Turing Centre for Living Systems, Aix-Marseille University,Marseille, France
  6. Janelia Research Campus,HHMI, Ashburn, VA USA
Institutions: Max Planck Florida Institute for Neuroscience (United States); Florida Atlantic University (United States); National Institute of Mental Health (United States); Aix-Marseille Université (France); Janelia Research Campus (United States)
Journal: Nature communications, volume 17, issue 1, article 8353
Dates: received 11 November 2025; accepted 11 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74869-1 · PMID 42409803 · PMCID PMC13473605 · OpenAlex W7167493109
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Synaptic plasticity, Premotor cortex, Neural circuits
MeSH: Learning*, Motor Cortex*, Neocortex*, Neuronal Plasticity*, Action Potentials, Animals, Calcium-Calmodulin-Dependent Protein Kinase Type 2, Male, Mice, Mice, Inbred C57BL, Neurons, Pyramidal Cells (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS (1DP2NS132108); Max Planck Society (NA); Esther A. and Joseph Klingenstein Fund (EAJK Fund) (NA); McKnight Endowment Fund for Neuroscience (MEFN) (NA); Kinship Foundation (NA)
Citations: not cited yet (Europe PMC); 126 references in the paper
Research resources: RRID:AB_143165, RRID:AB_221569, RRID:AB_2534095, RRID:AB_2535805, RRID:AB_2721906, RRID:AB_2797823, rabbit anti-RFP RRID:AB_591279, Ntsr1-Cre GN220 RRID:MMRRC_030648-UCD, GRP-Cre KH288 RRID:MMRRC_031183-UCD, Tlx-Cre PL56 RRID:MMRRC_041158-UCD

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

inagaki-lab/Majumder_et_al

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b9a84be50bbcfe06989e48e4cdf03c27d449387f, 5 July 2026
Languages: MATLAB (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Neurodata Without Borders (PyNWB, MatNWB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/inagaki-lab/Majumder_et_al

Zenodo 20314082

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: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-74869-1.

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Read it in the paper: doi.org/10.1038/s41467-026-74869-1.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 12 MeSH terms, 5 funders, 123 references, 10 RRIDs.

Cite

This paper

Majumder, S., Hirokawa, K., Yang, Z., Jain, A., Paletzki, R., Gerfen, C. R., Fontolan, L., Romani, S., Yasuda, R., & Inagaki, H. K. (2026). Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing. Nature communications, 17(1), 8353. https://doi.org/10.1038/s41467-026-74869-1

BibTeX

@article{majumder2026complementary,
author = {Majumder, Shouvik and Hirokawa, Koichi and Yang, Zidan and Jain, Anant and Paletzki, Ronald and Gerfen, Charles R. and Fontolan, Lorenzo and Romani, Sandro and Yasuda, Ryohei and Inagaki, Hidehiko K.},
title = {{Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8353},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74869-1},
url = {https://doi.org/10.1038/s41467-026-74869-1},
pmid = {42409803},
pmcid = {PMC13473605}
}

RIS

TY - JOUR
AU - Majumder, Shouvik
AU - Hirokawa, Koichi
AU - Yang, Zidan
AU - Jain, Anant
AU - Paletzki, Ronald
AU - Gerfen, Charles R.
AU - Fontolan, Lorenzo
AU - Romani, Sandro
AU - Yasuda, Ryohei
AU - Inagaki, Hidehiko K.
TI - Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/06
VL - 17
IS - 1
SP - 8353
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74869-1
UR - https://doi.org/10.1038/s41467-026-74869-1
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
"author": [
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"family": "Majumder",
"given": "Shouvik"
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"volume": "17",
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"page": "8353",
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"PMID": "42409803",
"PMCID": "PMC13473605",
"ISSN": "2041-1723",
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