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Perirhinal input to auditory cortex supports memory-guided sensory perception.

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  1. [1] § MATERIALS AND METHODS › Calcium imaging data analysis ↔ LG_activeVSsuppressed.m, lines 5 to 17 · score 0.57 · stimulus onset, SD, findpeak, threshold, baseline, active · Report a mismatch

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The authors' code

MATLAB · 225 lines · 7.2 KB · no license · 1 match

  1. dffmat = norm_all_Roi;
  2. dffmat = all_RoiSes; % data input here is a matrix with trial averaged activity for each ROI (rows). So each row in the matrix is the average of all trials for a single ROI
  3. nRois = size(dffmat,1);
  4. nFrames= size(dffmat,2);
  5. %% SET YOUR PARAMETERS HERE
  6. freqAcq = 30;
  8. stimOn = 2 *freqAcq; % define stimulus onset
  9. stimOff = 3 *freqAcq; % define stimulus offset . This is the time window where the calcium events are counted.
  10. peakdetect = 4 *freqAcq; % define offset for peak detection window
  11. eventduration = 0 *freqAcq; % define minimum event duration for detection
  12. peakDistance = 0.5 *freqAcq; % define minPeakDistance in the findpeak function
  13. peakWidth = 0;
  14. baseline = (0.1*freqAcq):(2*freqAcq); % define the baseline over which the threshold is calculated
  15. sd_value = 1.5;
  16. nbins = 2*freqAcq;
  17. roll_wind = 2*freqAcq;
  19. %%
  20. for i = 1:nRois
  21. ampl = [];
  22. ampl2 = [];
  23. w = [];
  24. w2 = [];
  25. locs = [];
  26. locs2 = [];
  27. trace_integral = [];
  29. ftrace = dffmat(i,:) ;
  31. % for k = 1:roll_wind
  32. % baseline_integral{1,k} = trapz((ftrace(k:nbins+k)));
  33. % int_threshold = sd_value* std((cell2mat(baseline_integral)));
  34. % end
  35. % % %
  36. baseline_integral = trapz((ftrace(baseline)));
  37. int_threshold = sd_value * (((baseline_integral)));
  39. peak_threshold = sd_value * (mean(ftrace(baseline)));
  40. trace_integral = trapz((ftrace(stimOn:peakdetect))); % this is the bit that actually measure th integral
  42. mean_f = [];
  44. if trace_integral > int_threshold
  45. active_log{1,i} = 1;
  46. [ampl,locs, w] = findpeaks((ftrace(stimOn:peakdetect)),'MinPeakheight',peak_threshold,'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth, 'Annotate','extents','WidthReference','halfheight');
  47. mean_f = mean(ftrace(stimOn:stimOff));
  48. elseif trace_integral < (int_threshold/sd_value)/2
  49. active_log{1,i} = 2;
  50. [ampl2,locs2, w2] = findpeaks((-ftrace(stimOn:peakdetect)),'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth, 'Annotate','extents','WidthReference','halfheight');
  51. else
  52. active_log{1,i} = NaN;
  53. end
  55. %
  56. %% plot
  57. % figure(i);
  58. % findpeaks(ftrace,'MinPeakheight',peak_threshold, 'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth,'Annotate','extents','WidthReference','halfheight'); hold on;
  59. % plot (locs+ stimOn , ampl +0.1 ,'o','MarkerSize',10,'MarkerFaceColor','b');
  60. % hold on;
  61. % plot (locs2+ stimOn , (-ampl2) +0.1 ,'o','MarkerSize',10,'MarkerFaceColor','r');
  62. % axis([0 nFrames -1 3 ]);
  63. % set(gca,'XTick',0:freqAcq:nFrames)
  64. % set(gca,'XTickLabel',0:1:nFrames/freqAcq)
  65. % title(['Roi ', num2str(i)]);
  67. %%
  69. if max(ampl)<0.1
  70. ampl=NaN;
  71. end
  72. %%
  74. all_Roi_ampl{i} = max(ampl'); % GIVES mean AMPLITUDES OF CALCIUM EVENTS FOR EACH ROIS
  75. all_Roi_ampl2{i} = max(ampl2'); %ampl of inhibited dendrites
  76. all_Roi_integral{i} = trace_integral;
  77. all_Roi_meanF{i} = mean_f;
  79. all_Roi_width {i} = (max(w)')/30;
  80. all_Roi_width2 {i} = (max(w2)')/30; %duration of inhibited dendrites
  81. all_Roi_locs {i} = (max(locs)')/ 30; % gives location of the peaks
  82. all_Roi_locs2 {i} = (max(locs2)')/ 30; % gives location of the peaks of inhibited dendrites
  83. %% data output
  85. AADATA = [ all_Roi_ampl' all_Roi_ampl2' all_Roi_integral' all_Roi_meanF' all_Roi_locs' ];
  87. inhibited = (sum(cell2mat(active_log)==2))/nRois;
  88. excited = (sum(cell2mat(active_log)==1))/nRois;
  89. nonResp = (sum(cell2mat(active_log)==0))/nRois;
  91. AAPercentage = [excited inhibited nonResp];
  95. end;
  97. %% PLOT
  99. % nRois=size(all_RoiSes,1);
  100. % nFrames=size(all_RoiSes,2);
  101. %
  102. % for r = 1:nRois;
  103. % f=figure(r);
  104. % hold on;
  105. % plot(dffmat(r,:),'g','LineWidth',1.5)
  106. % u=f.Renderer;
  107. % f.Renderer = 'painters'
  108. % set(gca,'XTick',0:30:nFrames+30);
  109. % set(gca,'XTickLabel',0:1:nFrames/30+30);
  110. % axis([0 170 -1 2]);
  111. % %
  112. % end
  114. %
  115. sort_mat_Roi =[dffmat cell2mat(active_log')];
  116. sort_col = size(sort_mat_Roi,2);
  117. sorted_all_Roi = sortrows(sort_mat_Roi,sort_col);
  118. dendrite_color_ID = sorted_all_Roi (:,size(sorted_all_Roi,2));
  119. sorted_all_Roi (:,size(sorted_all_Roi,2)) = [];
  121. %%
  123. all_dend = dffmat;
  125. for k=1:nRois
  127. if cell2mat(active_log(k)) == 1
  128. active_dend_v{k} = all_dend(k,:);
  129. integral_active{k} = cell2mat(AADATA(k,3));
  130. elseif cell2mat(active_log(k)) == 2
  131. inh_dend_v{k} = all_dend(k,:);
  132. integral_active{k} = [];
  133. else
  134. nr_dend_v{k} = all_dend(k,:);
  135. integral_active{k} = [];
  136. end
  137. end
  139. inh_dend=cell2mat(inh_dend_v');
  140. active_dend=cell2mat(active_dend_v');
  141. nr_dend=cell2mat(nr_dend_v');
  142. integral_active= (integral_active')
  143. sorttoheat = [inh_dend;active_dend; nr_dend];
  145. dendrite_color_ID = flip(dendrite_color_ID);
  147. %%
  150. % %
  151. %% plot heatmap
  152. f=figure(1);
  153. clims=([0 1]); imagesc(active_dend,clims);
  154. % colormap jet % colorbar
  155. set(gca,'XTick',0:30:nFrames)
  156. set(gca,'XTickLabel',0:1:nFrames/30)
  158. u=f.Renderer; f.Renderer = 'painters'
  159. axis([0 270 0 300]);
  161. %
  162. f=figure(2);
  163. clims=([0 2]);
  164. imagesc(dendrite_color_ID,clims);
  165. u=f.Renderer;
  166. f.Renderer = 'painters'
  168. %%
  170. faulty_id_log = active_dend<-0.5;
  172. faulty_id_logsum = sum(faulty_id_log ,2);
  174. faulty_id = find(faulty_id_logsum);
  176. active_dend(faulty_id,:)=[];
  177. % %%
  178. % figure
  179. % clustercategory = clustervis(active_dend,2);
  180. %
  181. % re_active = [active_dend clustercategory];
  182. %
  183. % cluster1 = active_dend(find(re_active(:,size(re_active,2)) == 1),:);
  184. % for g = 1:size(cluster1,1)
  185. % ftrace=cluster1(g,:);
  186. % [ampl1] = findpeaks((ftrace(stimOn:peakdetect)),'MinPeakheight',peak_threshold,'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth, 'Annotate','extents','WidthReference','halfheight');
  187. % cluster1_amplitues{g} = max(ampl1);
  188. % end
  189. %
  190. % cluster2 = active_dend(find(re_active(:,size(re_active,2)) == 2),:);
  191. % [ampl2] = findpeaks((cluster1(stimOn:peakdetect)),'MinPeakheight',peak_threshold,'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth, 'Annotate','extents','WidthReference','halfheight');
  192. % for e = 1:size(cluster2,1)
  193. % ftrace=cluster2(e,:);
  194. % [ampl2] = findpeaks((ftrace(stimOn:peakdetect)),'MinPeakheight',peak_threshold,'MinPeakDistance',peakDistance,'MinPeakWidth',peakWidth, 'Annotate','extents','WidthReference','halfheight');
  195. % cluster2_amplitues{e} = max(ampl2);
  196. % end
  198. %%
  199. %% %% plot mean trace with shade bar (std)
  200. %
  201. f=figure(2222);
  202. hold on;
  203. trace = active_dend;
  204. avg_all_Roi_trace = mean((trace));
  205. shadedErrorBar([],avg_all_Roi_trace, std((trace))/sqrt(size((trace),1)),'red',1);
  206. u=f.Renderer
  207. f.Renderer = 'painters'
  208. set(gca,'XTick',0:30:nFrames+30);
  209. set(gca,'XTickLabel',0:1:nFrames/30+30);
  210. axis([0 270 -0.1 1]);
  212. % f=figure(22222);
  213. % hold on;
  214. % trace = cluster2;
  215. % avg_all_Roi_trace = mean((trace));
  216. % shadedErrorBar([],avg_all_Roi_trace, std((trace))/sqrt(size((trace),1)),'r',1);
  217. % u=f.Renderer
  218. % f.Renderer = 'painters'
  219. % set(gca,'XTick',0:30:nFrames+30);
  220. % set(gca,'XTickLabel',0:1:nFrames/30+30);
  221. % axis([0 170 -0.1 3]);
  222. %

LG_activeVSsuppressed.m at commit 39af9f2, no license · at the source

Overview

Authors: Luca Godenzini1, Ann-Sofie Bjerre1,2, Yi Hu1, Lucy M Palmer1,3
ORCID iDs: Luca Godenzini, Ann-Sofie Bjerre, Yi Hu, Lucy M Palmer
  1. Florey Institute of Neuroscience and Mental Health, Parkville, Victoria 3000, Australia
  2. School of Medicine, University of Tasmania, Hobart, Tasmania 7000, Australia
  3. Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3000, Australia
Institutions: Florey Institute of Neuroscience and Mental Health (Australia); University of Tasmania (Australia); The University of Melbourne (Australia)
Journal: Science advances, volume 12, issue 21, article eaed4808
Dates: received 29 October 2025; accepted 14 April 2026; published online 22 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aed4808 · PMID 42172314 · PMCID PMC13196781 · OpenAlex W7162087907
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism)
Methods: Statistics, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
MeSH: Auditory Cortex*, Auditory Perception*, Memory*, Perirhinal Cortex*, Animals, Learning, Mice, Pyramidal Cells (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Sylvia and Charles Viertel Charitable Foundation; Australian Research Council (DP160103047); National Health and Medical Research Council (2003114)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Through learning and memory, the cortex dynamically forms sensory associations to guide our behavior in an ever-changing environment. How memory-related brain regions, such as the medial-temporal lobe, influence sensory cortex to support this process remains unclear. Here, we used two-photon calcium imaging of axonal projections from the perirhinal (PRh) cortex to the auditory cortex during learning and generalization of an auditory discrimination task. PRh input in the auditory cortex increased during learning and influenced performance in a categorization task. Chemogenetically silencing PRh input selectively disrupted the activity of auditory cortex layer 2/3 pyramidal neurons, and PRh photoactivation drove behavior toward overgeneralization. These findings highlight PRh input as a key modulatory pathway that shapes cortical dynamics underlying auditory learning and memory-guided sensory perception.

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 1 match between paragraphs and lines of code.

Zenodo 19108600

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (4 files), shadedErrorBar (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
8 files
At the source: dx.doi.org/10.5281/zenodo.19108600

lucagode/perirhinal-manuscript-code

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 39af9f216e54406bcf1d74a36b5b53b98788df64, 20 September 2024
Languages: MATLAB (8)
Size: 8 files, 8 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (4 files), shadedErrorBar (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/LucaGode/Perirhinal-manuscript-code/tree/March26

The paper's code and data availability statement is in the Data section.

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Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. The codes used to analyze the data in the paper are available at DOI: 10.5281/zenodo.19108600 (http://dx.doi.org/10.5281/zenodo.19108600).

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Version 2, 28 September 2026

  • Funding: added Sylvia and Charles Viertel Charitable Foundation; Australian Research Council: DP160103047; National Health and Medical Research Council: 2003114

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 MeSH terms, 59 references.

Cite

This paper

Godenzini, L., Bjerre, A.-S., Hu, Y., & Palmer, L. M. (2026). Perirhinal input to auditory cortex supports memory-guided sensory perception. Science advances, 12(21), eaed4808. https://doi.org/10.1126/sciadv.aed4808

BibTeX

@article{godenzini2026perirhinal,
author = {Godenzini, Luca and Bjerre, Ann-Sofie and Hu, Yi and Palmer, Lucy M},
title = {{Perirhinal input to auditory cortex supports memory-guided sensory perception}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {21},
pages = {eaed4808},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aed4808},
url = {https://doi.org/10.1126/sciadv.aed4808},
pmid = {42172314},
pmcid = {PMC13196781}
}

RIS

TY - JOUR
AU - Godenzini, Luca
AU - Bjerre, Ann-Sofie
AU - Hu, Yi
AU - Palmer, Lucy M
TI - Perirhinal input to auditory cortex supports memory-guided sensory perception
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/22
VL - 12
IS - 21
SP - eaed4808
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aed4808
UR - https://doi.org/10.1126/sciadv.aed4808
LA - en
ER -

CSL-JSON

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"title": "Perirhinal input to auditory cortex supports memory-guided sensory perception",
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"family": "Godenzini",
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{
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}
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"container-title-short": "Sci Adv",
"volume": "12",
"issue": "21",
"page": "eaed4808",
"DOI": "10.1126/sciadv.aed4808",
"PMID": "42172314",
"PMCID": "PMC13196781",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
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"language": "en",
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2026,
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