Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.

Code ↔ Paper

6 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 6 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted

[1] § Methods › In vivo electrophysiology › LFP analysis ↔ Coherence.m, lines 26 to 60 · score 0.84 · 12–30 Hz, 4–12 Hz, 30–50 Hz, 50–100 Hz, 1–4 Hz, PSD · Report a mismatch
[2] § Methods › In vivo electrophysiology › LFP analysis ↔ getPSD.m, the whole file · a weak match · score 0.75 · 30–50 Hz, 50–100 Hz, beta, mtspectrumsegc, 1–4 Hz, delta · Report a mismatch
[3] § Methods › In vivo electrophysiology › LFP analysis ↔ FindRipples.m, lines 1 to 45 · score 0.70 · peak normalized power, standard deviations, discarded, intervals, ripple, band · Report a mismatch
[4] § Methods › In vivo electrophysiology › Testing optogenetic response ↔ optotagging_forpv.m, lines 11 to 69 · score 0.65 · peak half height, Zeta, onset, stimulation, crossing, tagging · Report a mismatch
[5] § Methods › In vivo electrophysiology › Data acquisition and processing ↔ FindRipples.m, lines 1 to 45 · score 0.55 · standard deviations, Hz, noise, signals, filter, thresholding · Report a mismatch
[6] § Results › Enhanced MS PV+ neuronal firing during object exploration ↔ optotagging_forpv.m, lines 11 to 69 · score 0.53 · peak half height, row, Raster, stimulus, median, traces · Report a mismatch

Paper

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The authors' code

MATLAB · 322 lines · 11 KB · no license · 2 matches

function [ripples,sd,bad] = FindRipples(filtered,varargin)
%FindRipples - Find hippocampal ripples (100~200Hz oscillations).
%
% USAGE
%
% [ripples,stdev,noise] = FindRipples(filtered,<options>)
%
% Ripples are detected using the normalized squared signal (NSS) by
% thresholding the baseline, merging neighboring events, thresholding
% the peaks, and discarding events with excessive duration.
% Thresholds are computed as multiples of the standard deviation of
% the NSS. Alternatively, one can use explicit values, typically obtained
% from a previous call.
%
% filtered ripple-band filtered LFP (one channel).
% <options> optional list of property-value pairs (see table below)
%
% =========================================================================
% Properties Values
% -------------------------------------------------------------------------
% 'thresholds' thresholds for ripple beginning/end and peak, in multiples
% of the stdev (default = [2 5])
% 'durations' min inter-ripple interval, max ripple duration and min ripple duration, in ms
% (default = [30 100 20])
% 'baseline' interval used to compute normalization (default = all)
% 'restrict' same as 'baseline' (for backwards compatibility)
% 'frequency' sampling rate (in Hz) (default = 1250Hz)
% 'stdev' reuse previously computed stdev
% 'show' plot results (default = 'off')
% 'noise' noisy ripple-band filtered channel used to exclude ripple-
% like noise (events also present on this channel are
% discarded)
% =========================================================================
%
% OUTPUT
%
% ripples for each ripple, [start_t peak_t end_t peakNormalizedPower]
% stdev standard deviation of the NSS (can be reused subsequently)
% noise ripple-like activity recorded simultaneously on the noise
% channel (for debugging info)
%
% SEE
%
% See also FilterLFP, RippleStats, SaveRippleEvents, PlotRippleStats.
% Copyright (C) 2004-2011 by Michaël Zugaro, initial algorithm by Hajime Hirase
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 3 of the License, or
% (at your option) any later version.
% Default values
frequency = 1250;
show = 'off';
restrict = [];
sd = [];
stdev=1
lowThresholdFactor = 2; % Ripple envoloppe must exceed lowThresholdFactor*stdev
highThresholdFactor = 5; % Ripple peak must exceed highThresholdFactor*stdev
minInterRippleInterval = 30; % in ms
maxRippleDuration = 100; % in ms
minRippleDuration = 20; % in ms
noise = [];
% Check number of parameters
if nargin < 1 | mod(length(varargin),2) ~= 0,
error('Incorrect number of parameters (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
% Check parameter sizes
if ~isdmatrix(filtered) | size(filtered,2) ~= 2,
error('Parameter ''filtered'' is not a Nx2 matrix (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
% Parse parameter list
for i = 1:2:length(varargin),
if ~ischar(varargin{i}),
error(['Parameter ' num2str(i+2) ' is not a property (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).']);
end
switch(lower(varargin{i})),
case 'thresholds',
thresholds = varargin{i+1};
if ~isivector(thresholds,'#2','>0'),
error('Incorrect value for property ''thresholds'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
lowThresholdFactor = thresholds(1);
highThresholdFactor = thresholds(2);
case 'durations',
durations = varargin{i+1};
if ~isivector(durations,'#3','>0'),
error('Incorrect value for property ''durations'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
minInterRippleInterval = durations(1);
maxRippleDuration = durations(2);
minRippleDuration = durations(3);
case 'frequency',
frequency = varargin{i+1};
if ~isdscalar(frequency,'>0'),
error('Incorrect value for property ''frequency'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
case 'show',
show = varargin{i+1};
if ~isstring(show,'on','off'),
error('Incorrect value for property ''show'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
case {'baseline','restrict'},
restrict = varargin{i+1};
if ~isempty(restrict) & ~isdvector(restrict,'#2','<'),
error('Incorrect value for property ''restrict'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
case 'stdev',
sd = varargin{i+1};
if ~isdscalar(sd,'>0'),
error('Incorrect value for property ''stdev'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
case 'noise',
noise = varargin{i+1};
if ~isdmatrix(noise) | size(noise,1) ~= size(filtered,1) | size(noise,2) ~= 2,
error('Incorrect value for property ''noise'' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).');
end
otherwise,
error(['Unknown property ''' num2str(varargin{i}) ''' (type ''help <a href="matlab:help FindRipples">FindRipples</a>'' for details).']);
end
end
% Parameters
windowLength = round(frequency/1250*11);
% Square and normalize signal
signal = filtered(:,2);
squaredSignal = signal.^2;
window = ones(windowLength,1)/windowLength;
keep = [];
if ~isempty(restrict),
keep = filtered(:,1)>=restrict(1)&filtered(:,1)<=restrict(2);
end
[normalizedSquaredSignal,sd] = unity(Filter0(window,sum(squaredSignal,2)),sd,keep);
% Detect ripple periods by thresholding normalized squared signal
thresholded = normalizedSquaredSignal > lowThresholdFactor;
start = find(diff(thresholded)>0);
stop = find(diff(thresholded)<0);
% Exclude last ripple if it is incomplete
if length(stop) == length(start)-1,
start = start(1:end-1);
end
% Exclude first ripple if it is incomplete
if length(stop)-1 == length(start),
stop = stop(2:end);
end
% Correct special case when both first and last ripples are incomplete
if start(1) > stop(1),
stop(1) = [];
start(end) = [];
end
firstPass = [start,stop];
if isempty(firstPass),
disp('Detection by thresholding failed');
return
else
disp(['After detection by thresholding: ' num2str(length(firstPass)) ' events.']);
end
% Merge ripples if inter-ripple period is too short
minInterRippleSamples = minInterRippleInterval/1000*frequency;
secondPass = [];
ripple = firstPass(1,:);
for i = 2:size(firstPass,1)
if firstPass(i,1) - ripple(2) < minInterRippleSamples,
% Merge
ripple = [ripple(1) firstPass(i,2)];
else
secondPass = [secondPass ; ripple];
ripple = firstPass(i,:);
end
end
secondPass = [secondPass ; ripple];
if isempty(secondPass),
disp('Ripple merge failed');
return
else
disp(['After ripple merge: ' num2str(length(secondPass)) ' events.']);
end
% Discard ripples with a peak power < highThresholdFactor
thirdPass = [];
peakNormalizedPower = [];
for i = 1:size(secondPass,1)
[maxValue,maxIndex] = max(normalizedSquaredSignal([secondPass(i,1):secondPass(i,2)]));
if maxValue > highThresholdFactor,
thirdPass = [thirdPass ; secondPass(i,:)];
peakNormalizedPower = [peakNormalizedPower ; maxValue];
end
end
if isempty(thirdPass),
disp('Peak thresholding failed.');
return
else
disp(['After peak thresholding: ' num2str(length(thirdPass)) ' events.']);
end
% Detect negative peak position for each ripple
peakPosition = zeros(size(thirdPass,1),1);
for i=1:size(thirdPass,1),
[minValue,minIndex] = min(signal(thirdPass(i,1):thirdPass(i,2)));
peakPosition(i) = minIndex + thirdPass(i,1) - 1;
end
% Discard ripples that are way too long
time = filtered(:,1);
ripples = [time(thirdPass(:,1)) time(peakPosition) time(thirdPass(:,2)) peakNormalizedPower];
duration = ripples(:,3)-ripples(:,1);
ripples(duration>maxRippleDuration/1000,:) = [];
disp(['After max duration test: ' num2str(size(ripples,1)) ' events.']);
%Discard ripples that are way too short
duration = ripples(:,3)-ripples(:,1);
ripples(duration<minRippleDuration/1000,:) = [];
disp(['After min duration test: ' num2str(size(ripples,1)) ' events.']);
% If a noisy channel was provided, find ripple-like events and exclude them
bad = [];
if ~isempty(noise),
% Square and pseudo-normalize (divide by signal stdev) noise
squaredNoise = noise(:,2).^2;
window = ones(windowLength,1)/windowLength;
normalizedSquaredNoise = unity(Filter0(window,sum(squaredNoise,2)),sd,[]);
excluded = logical(zeros(size(ripples,1),1));
% Exclude ripples when concomittent noise crosses high detection threshold
previous = 1;
for i = 1:size(ripples,1),
j = FindInInterval(noise,[ripples(i,1),ripples(i,3)],previous);
previous = j(2);
if any(normalizedSquaredNoise(j(1):j(2))>(highThresholdFactor)), %Changed to highThresholdFactor-2 to increase sensitivity of noise channel
excluded(i) = 1;
end
end
bad = ripples(excluded,:);
ripples = ripples(~excluded,:);
disp(['After noise removal: ' num2str(size(ripples,1)) ' events.']);
end
% Optionally, plot results
if strcmp(show,'on'),
figure;
if ~isempty(noise),
MultiPlotXY([time signal],[time squaredSignal],[time normalizedSquaredSignal],[time noise(:,2)],[time squaredNoise],[time normalizedSquaredNoise]);
nPlots = 6;
subplot(nPlots,1,3);
ylim([0 highThresholdFactor*1.1]);
subplot(nPlots,1,6);
ylim([0 highThresholdFactor*1.1]);
else
MultiPlotXY([time signal],[time squaredSignal],[time normalizedSquaredSignal]);
% MultiPlotXY(time,signal,time,squaredSignal,time,normalizedSquaredSignal);
nPlots = 3;
subplot(nPlots,1,3);
ylim([0 highThresholdFactor*1.1]);
end
for i = 1:nPlots,
subplot(nPlots,1,i);
hold on;
yLim = ylim;
for j=1:size(ripples,1),
plot([ripples(j,1) ripples(j,1)],yLim,'g-');
plot([ripples(j,2) ripples(j,2)],yLim,'k-');
plot([ripples(j,3) ripples(j,3)],yLim,'r-');
if i == 3,
plot([ripples(j,1) ripples(j,3)],[ripples(j,4) ripples(j,4)],'k-');
end
end
for j=1:size(bad,1),
plot([bad(j,1) bad(j,1)],yLim,'k-');
plot([bad(j,2) bad(j,2)],yLim,'k-');
plot([bad(j,3) bad(j,3)],yLim,'k-');
if i == 3,
plot([bad(j,1) bad(j,3)],[bad(j,4) bad(j,4)],'k-');
end
end
if mod(i,3) == 0,
plot(xlim,[lowThresholdFactor lowThresholdFactor],'k','linestyle','--');
plot(xlim,[highThresholdFactor highThresholdFactor],'k-');
end
end
end
function y = Filter0(b,x)
if size(x,1) == 1,
x = x(:);
end
if mod(length(b),2) ~= 1,
error('filter order should be odd');
end
shift = (length(b)-1)/2;
[y0 z] = filter(b,1,x);
y = [y0(shift+1:end,:) ; z(1:shift,:)];
function [U,stdA] = unity(A,sd,restrict)
if ~isempty(restrict),
meanA = mean(A(restrict));
stdA = std(A(restrict));
else
meanA = mean(A);
stdA = std(A);
end
if ~isempty(sd),
stdA = sd;
end
U = (A - meanA)/stdA;

FindRipples.m at commit ba5a476, no license · at the source

Overview

Authors: Yawen Zheng¹, Jifu Tong¹, Yuanwei Xing¹, Shan Shao¹, Naizheng Liu¹, Shuting Liu¹, Jiao Wu¹, Wang Yin¹, Jiaxin Wang¹, Shulu Yuan¹, Kun Cui¹, Xuetao Qi¹, Shuang Cui¹, You Wan^1,2,3,4, Ming Yi^1,2,5

ORCID iDs: Yawen Zheng, Jifu Tong, Xuetao Qi, You Wan

Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing, 100083 China
Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Peking University, Beijing, 100083 China
Beijing Life Science Academy, Beijing, 102209 China
Co-innovation Center of Neuroregeneration, Nantong University, Nantong, 226007 China
Medical Innovation Center (Taizhou)of Peking University, Taizhou, 225316 China

Institutions: Peking University (China); Nantong University (China)

Journal: Nature communications, volume 17, issue 1, article 4259

Dates: received 21 September 2024; accepted 23 February 2026; published online 20 March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41467-026-70268-8 · PMID 41862484 · PMCID PMC13168293 · OpenAlex W7138880311

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism), cognitive (subfield)

Methods: Preprocessing, Statistics, Machine learning, Spectral & time-frequency, Connectivity, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures

Keywords: Spatial memory, Cognitive control

MeSH: Hippocampus*, Neurons*, Parvalbumins*, Septal Nuclei*, Spatial Memory*, Animals, CA1 Region, Hippocampal, Male, Mice, Mice, Inbred C57BL, Optogenetics, Septum of Brain (* major topic)

Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (National Science Foundation of China) (32371049, 32271053)

Citations: not cited yet (Europe PMC); 70 references in the paper

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.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

evelynzyw/evelyn_PV_analysis

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: ba5a4761f8bc928803e623b33ce8c7af89d3f5ba, 26 January 2026

Languages: MATLAB (9)

Size: 9 files, 9 scripts

Software Heritage: not archived

Found in: “Code availability”

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

Tools: Chronux (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

9 files

Coherence.m, MATLAB, 65 lines, 1 match
FilterLFP.m, MATLAB, 93 lines
FindRipples.m, MATLAB, 322 lines, 2 matches
getPSD.m, MATLAB, 51 lines, 1 match
getripple.m, MATLAB, 19 lines
getspike.m, MATLAB, 31 lines
optotagging_forpv.m, MATLAB, 69 lines, 2 matches
readedf_eegemg.m, MATLAB, 13 lines
watson_u2_test.m, MATLAB, 73 lines

At the source: github.com/evelynzyw/evelyn_PV_analysis

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: evelynzyw/evelyn_PV_analysis

Read it in the paper: doi.org/10.1038/s41467-026-70268-8.

Tracing map

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

data.mendeley.com/datasets/c872f83pdz, at Mendeley Data; found in the references
figshare:27852507, at figshare; found in “Data availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: figshare 27852507
it points to the authors' code: evelynzyw/evelyn_PV_analysis

Read it in the paper: doi.org/10.1038/s41467-026-70268-8.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 12 MeSH terms, 1 funder, 68 references.

Cite

This paper

Zheng, Y., Tong, J., Xing, Y., Shao, S., Liu, N., Liu, S., Wu, J., Yin, W., Wang, J., Yuan, S., Cui, K., Qi, X., Cui, S., Wan, Y., & Yi, M. (2026). Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory. Nature communications, 17(1), 4259. https://doi.org/10.1038/s41467-026-70268-8

BibTeX

@article{zheng2026parvalbumin, author = {Zheng, Yawen and Tong, Jifu and Xing, Yuanwei and Shao, Shan and Liu, Naizheng and Liu, Shuting and Wu, Jiao and Yin, Wang and Wang, Jiaxin and Yuan, Shulu and Cui, Kun and Qi, Xuetao and Cui, Shuang and Wan, You and Yi, Ming}, title = {{Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory}}, journal = {Nature communications}, year = {2026}, month = mar, volume = {17}, number = {1}, pages = {4259}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-70268-8}, url = {https://doi.org/10.1038/s41467-026-70268-8}, pmid = {41862484}, pmcid = {PMC13168293} }

RIS

TY - JOUR AU - Zheng, Yawen AU - Tong, Jifu AU - Xing, Yuanwei AU - Shao, Shan AU - Liu, Naizheng AU - Liu, Shuting AU - Wu, Jiao AU - Yin, Wang AU - Wang, Jiaxin AU - Yuan, Shulu AU - Cui, Kun AU - Qi, Xuetao AU - Cui, Shuang AU - Wan, You AU - Yi, Ming TI - Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/03/20 VL - 17 IS - 1 SP - 4259 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-70268-8 UR - https://doi.org/10.1038/s41467-026-70268-8 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-70268-8", "type": "article-journal", "title": "Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory", "container-title": "Nature communications", "author": [ { "family": "Zheng", "given": "Yawen" }, { "family": "Tong", "given": "Jifu" }, { "family": "Xing", "given": "Yuanwei" }, { "family": "Shao", "given": "Shan" }, { "family": "Liu", "given": "Naizheng" }, { "family": "Liu", "given": "Shuting" }, { "family": "Wu", "given": "Jiao" }, { "family": "Yin", "given": "Wang" }, { "family": "Wang", "given": "Jiaxin" }, { "family": "Yuan", "given": "Shulu" }, { "family": "Cui", "given": "Kun" }, { "family": "Qi", "given": "Xuetao" }, { "family": "Cui", "given": "Shuang" }, { "family": "Wan", "given": "You" }, { "family": "Yi", "given": "Ming" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "4259", "DOI": "10.1038/s41467-026-70268-8", "PMID": "41862484", "PMCID": "PMC13168293", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-70268-8", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 20 ] ] } }

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