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Stable readout of visual representations mediates flexible generalization.

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  1. [1] § Methods › Method details › Electrophysiological recordings and response epochs ↔ supp3_tuning.m, lines 166–233 · score 0.55 · 50–550 ms, channel, spike, 250 Hz, 200 Hz, 200 ms
  2. [2] § Methods › Method details › Shape stimulus generation ↔ shapegen_3d_medaxis/gen_shape.m, lines 1–35 · score 0.53 · medial axis, global, gloss, twist, smoothed, curved

Paper

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The authors' code

MATLAB · 233 lines · 10 KB · MIT · 1 match

  1. clc; close all; clear;
  2. load('data/supp3_tuning.mat')
  4. %%
  5. figure('color','w','Position',[94,316,895,508])
  6. % ====== random trial raster ================
  7. randTrial = 291; % randi(length(params));
  8. % set 14, curv 0.9074, sel 0.1853
  9. hRaster = subplot(4,4,[1 2 5 6 9 10]);
  10. hPsth = subplot(4,4,[13 14]);
  11. plotRaster(hRaster,hPsth,params(randTrial),eid)
  13. % ====== random unit tuning example v4 ======
  14. sets = unique([params.set]); % sets = sets(2:3);
  15. cols = [187 135 134; 142 230 232; 158 208 150; ]/255; % autumn(length(sets));
  17. subplot(244); hold on; cla;
  18. randUnitId = 46; % randi(size(v4sites_stim,2));
  19. for ii=1:length(sets)
  20. cc = [params([params.set] == sets(ii)).curv];
  22. nnresp_stim = v4sites_stim([params.set] == sets(ii),randUnitId);
  23. % nnresp_stim = nnresp_stim-mean(respBase.v4sites(:,randUnitId));
  24. plotTrendPatchLine(gca,cc,nnresp_stim',cols(ii,:),true);
  26. nnresp_arc = v4sites_arc([params.set] == sets(ii),randUnitId);
  27. % nnresp_arc = nnresp_arc-mean(respBase.v4sites(:,randUnitId));
  28. plotTrendPatchLine(gca,cc,nnresp_arc',0.8*cols(ii,:),true);
  29. end
  30. fixPlot(gca,[-0.1 1.1],[30 270],'curvature','response',0:0.25:1,0:50:250,'example v4 unit');
  31. % {'' 'shape 13' '' 'shape 13+arc' '' 'shape 14' '' 'shape 14+arc'})
  32. % legend('box','on','location','northwest')
  34. % ====== random unit tuning example v1 ======
  35. subplot(243); hold on; cla;
  36. randUnitId = 11; % randi(size(v1sites_stim,2));
  37. for ii=1:length(sets)
  38. cc = [params([params.set] == sets(ii)).curv];
  40. nnresp_stim = v1sites_stim([params.set] == sets(ii),randUnitId);
  41. % nnresp_stim = nnresp_stim-mean(respBase.v1sites(:,randUnitId));
  42. plotTrendPatchLine(gca,cc,nnresp_stim',cols(ii,:),true);
  44. nnresp_arc = v1sites_arc([params.set] == sets(ii),randUnitId);
  45. % nnresp_arc = nnresp_arc-mean(respBase.v1sites(:,randUnitId));
  46. plotTrendPatchLine(gca,cc,nnresp_arc',0.8*cols(ii,:),true);
  47. end
  48. fixPlot(gca,[-0.1 1.1],[35 215],'curvature','response',0:0.25:1,0:50:250,'example v1 unit')
  49. % legend('box','on','location','northwest')
  51. % ====== selectivity for this session ======
  52. sets = unique([params.set]); % sets = sets(2:3);
  53. selId_v4_stim = nan(size(v4sites_stim,2),length(sets));
  54. selId_v4_arc = nan(size(v4sites_stim,2),length(sets));
  55. selId_v1_stim = nan(size(v1sites_stim,2),length(sets));
  56. selId_v1_arc = nan(size(v1sites_stim,2),length(sets));
  57. for ii=1:length(sets)
  58. cc = [params([params.set] == sets(ii)).curv];
  59. for nn=1:size(v4sites_stim,2)
  60. nnresp_stim = v4sites_stim([params.set] == sets(ii),nn);
  61. % nnresp_stim = nnresp_stim-mean(respBase.v4sites(:,nn));
  62. [~,nnresp_stim] = plotTrendPatchLine([],cc,nnresp_stim',[],true);
  63. selId_v4_stim(nn,ii) = (max(nnresp_stim)-min(nnresp_stim))./(max(nnresp_stim)+min(nnresp_stim));
  64. [nnresp_stim_max,idx] = max(nnresp_stim);
  66. nnresp_arc = v4sites_arc([params.set] == sets(ii),nn);
  67. % nnresp_arc = nnresp_arc-mean(respBase.v4sites(:,nn));
  68. [~,nnresp_arc] = plotTrendPatchLine([],cc,nnresp_arc',[],true);
  69. % selId_v4_arc(nn,ii) = (max(nnresp_arc)-min(nnresp_arc))./(max(nnresp_arc)+min(nnresp_arc));
  70. % selId_v4_arc(nn,ii) = (max(nnresp_arc)-max(nnresp_stim))./(max(nnresp_arc)+max(nnresp_stim));
  71. selId_v4_arc(nn,ii) = (nnresp_stim_max-nnresp_arc(idx))./(nnresp_stim_max+nnresp_arc(idx));
  72. end
  74. for nn=1:size(v1sites_stim,2)
  75. nnresp_stim = v1sites_stim([params.set] == sets(ii),nn);
  76. % nnresp_stim = nnresp_stim-mean(respBase.v1sites(:,nn));
  77. [~,nnresp_stim] = plotTrendPatchLine([],cc,nnresp_stim',[],true);
  78. selId_v1_stim(nn,ii) = (max(nnresp_stim)-min(nnresp_stim))./(max(nnresp_stim)+min(nnresp_stim));
  79. [nnresp_stim_max,idx] = max(nnresp_stim);
  81. nnresp_arc = v1sites_arc([params.set] == sets(ii),nn);
  82. % nnresp_arc = nnresp_arc-mean(respBase.v1sites(:,nn));
  83. [~,nnresp_arc] = plotTrendPatchLine([],cc,nnresp_arc',[],true);
  84. % selId_v1_arc(nn,ii) = (max(nnresp_arc)-min(nnresp_arc))./(max(nnresp_arc)+min(nnresp_arc));
  85. % selId_v1_arc(nn,ii) = (max(nnresp_arc)-max(nnresp_stim))./(max(nnresp_arc)+max(nnresp_stim));
  86. selId_v1_arc(nn,ii) = (nnresp_stim_max-nnresp_arc(idx))./(nnresp_stim_max+nnresp_arc(idx));
  87. end
  88. end
  89. subplot(247); hold on;
  90. % plot(selId_v1_stim(:),abs(selId_v1_arc(:)),'.','color',[0.2 0.5 0.9],'markersize',12)
  91. % plot(selId_v4_stim(:),abs(selId_v4_arc(:)),'.','color',[0.9 0.5 0.2],'markersize',12)
  92. scatter(abs(selId_v1_stim(:)),abs(selId_v1_arc(:)),20,[0.2 0.5 0.9],'filled','MarkerFaceAlpha',1)
  93. fixPlot(gca,[-0.04 0.44],[-0.04 0.44],'curvature selectivity','curvature+arc selectivity',0:0.2:1,0:0.2:1,'v1')
  94. subplot(248); hold on;
  95. scatter(abs(selId_v4_stim(:)),abs(selId_v4_arc(:)),20,[0.9 0.5 0.2],'filled','MarkerFaceAlpha',1)
  96. fixPlot(gca,[-0.04 0.44],[-0.04 0.44],'curvature selectivity','curvature+arc selectivity',0:0.2:1,0:0.2:1,'v4')
  98. %% selectivity across sessions
  99. load('data/supp3_selectivity.mat','selId_v4_arc_all','selId_v1_arc_all','selId_v4_stim_all','selId_v1_stim_all');
  101. idx = (selId_v1_stim_all==1 | selId_v1_arc_all==1);
  102. selId_v1_stim_all(idx) = [];
  103. selId_v1_arc_all(idx) = [];
  104. idx = (selId_v4_stim_all==1 | selId_v4_arc_all==1);
  105. selId_v4_stim_all(idx) = [];
  106. selId_v4_arc_all(idx) = [];
  108. figure('color','w','pos',[390,410,794,329])
  109. subplot(245); hold on;
  110. plot(selId_v1_stim_all,selId_v1_arc_all,'.','color',[0.2 0.5 0.9],'markersize',10)
  111. plot(nanmean(selId_v1_stim_all),nanmean(selId_v1_arc_all),'+','markersize',15,'linewidth',1,'color',0.3*[0.2 0.5 0.9]);
  112. line([0 1],[0 1],'linestyle','--','color','k','linewidth',2);
  114. beta = regress(selId_v1_arc_all,[ones(size(selId_v1_stim_all,1),1) selId_v1_stim_all]);
  115. line([0 1],[beta(1) sum(beta)],'linestyle','--','color','r','linewidth',2);
  117. fixPlot(gca,[0 1],[-0.5 1],'curvature selectivity','curvature+arc selectivity',0:0.5:1,-0.5:0.5:1,'v1')
  119. % binE = -0.0125:0.025:1.0125;
  120. % binC = (binE+circshift(binE,-1))/2; binC = binC(1:end-1);
  121. % [cx,cy] = meshgrid(binC,binC);
  122. % clevels = 50:100:1000;
  123. % a = histcounts2(selId_v1_stim_all,selId_v1_arc_all,binE,binE);
  124. % [~,ha] = contour(cx,cy,a,clevels);
  126. subplot(241);
  127. histogram(selId_v1_stim_all,linspace(0,1,25),'DisplayStyle','stairs','EdgeColor',[0.2 0.5 0.9],'linewidth',2)
  128. fixPlot(gca,[0 1],[0 12000],'','',0:0.5:1,[])
  130. subplot(246);
  131. histogram(selId_v1_arc_all,linspace(-0.5,1,25),'DisplayStyle','stairs','EdgeColor',[0.2 0.5 0.9],'linewidth',2)
  132. fixPlot(gca,[-0.5 1],[0 12000],'','',-0.5:0.5:1,[])
  134. subplot(247); hold on;
  135. plot(selId_v4_stim_all,selId_v4_arc_all,'.','color',[0.9 0.5 0.2],'markersize',10)
  136. plot(nanmean(selId_v4_stim_all),nanmean(selId_v4_arc_all),'+','markersize',15,'linewidth',1,'color',0.3*[0.9 0.5 0.2]);
  137. line([0 1],[0 1],'linestyle','--','color','k','linewidth',2);
  138. beta = regress(selId_v4_arc_all,[ones(size(selId_v4_stim_all,1),1) selId_v4_stim_all]);
  139. line([0 1],[beta(1) sum(beta)],'linestyle','--','color','r','linewidth',2);
  140. fixPlot(gca,[0 1],[-0.5 1],'curvature selectivity','curvature+arc selectivity',0:0.5:1,-0.5:0.5:1,'v4')
  142. % a = histcounts2(abs(selId_v4_stim_all),abs(selId_v4_arc_all),binE,binE);
  143. % contour(cx,cy,a,clevels)
  145. subplot(243);
  146. histogram(selId_v4_stim_all,linspace(0,1,25),'DisplayStyle','stairs','EdgeColor',[0.9 0.5 0.2],'linewidth',2)
  147. fixPlot(gca,[0 1],[0 12000],'','',0:0.5:1,[])
  149. subplot(248);
  150. histogram(selId_v4_arc_all,linspace(-0.5,1,25),'DisplayStyle','stairs','EdgeColor',[0.9 0.5 0.2],'linewidth',2)
  151. fixPlot(gca,[-0.5 1],[0 12000],'','',-0.5:0.5:1,[])
  153. %% functions
  154. function [xx_u,yy_m] = plotTrendPatchLine(h,xVar,yVar,col,doSmooth)
  155. [xx_u,~,grp] = unique(xVar);
  156. yy_m = groupsummary(yVar',grp,'mean'); if doSmooth; yy_m = smooth(yy_m); end
  157. yy_s = groupsummary(yVar',grp,'std')./sqrt(groupsummary(yVar',grp,'nnz'));
  159. if ~isempty(h)
  160. hold(h,'on')
  161. patch([xx_u fliplr(xx_u)]',[yy_m-yy_s/2; flipud(yy_m+yy_s/2)],col,'edgecolor','none','facealpha',0.5,'parent',h)
  162. plot(h,xx_u,yy_m,'linewidth',2,'color',col);
  163. end
  164. end
  166. function plotRaster(hRaster,hPsth,param_trial,eid)
  167. tMarks = [0 param_trial.time.stimOn param_trial.time.fixStayAfterTargOn param_trial.time.targStay param_trial.time.corrStay];
  168. tMarks = [-param_trial.time.fixStay cumsum(tMarks)];
  170. cols = [0.9 0.5 0.2; 0.2 0.5 0.9];
  171. hold(hRaster,'on');
  173. gSites = find(eid(:,1) == 1);
  174. pl = ismember(param_trial.spikes(:,2),gSites);
  175. v4s = param_trial.spikes(pl,:);
  176. [~,~,a] = unique(v4s(:,2)); v4s(:,2) = a;
  177. % plot(v4s(:,1),v4s(:,2),'b.','color',cols(1,:))
  178. scatter(hRaster,v4s(:,1),v4s(:,2),7,cols(1,:),'filled','markerfacealpha',1);
  180. gSites = find(eid(:,1) == 2);
  181. pl = ismember(param_trial.spikes(:,2),gSites);
  182. v1s = param_trial.spikes(pl,:);
  183. [~,~,a] = unique(v1s(:,2)); v1s(:,2) = a+max(v4s(:,2));
  184. scatter(hRaster,v1s(:,1),v1s(:,2),7,cols(2,:),'filled','markerfacealpha',1);
  186. arrayfun(@(jj) line(hRaster,[tMarks(jj) tMarks(jj)],[1 256],'linewidth',2,'color','k'),1:length(tMarks))
  187. tMax = round(max(tMarks),-2)+200;
  188. tMax = 950;
  190. % t = [floor(min([v4s(:,1);v1s(:,1);sas(:,1)])) ceil(max([v4s(:,1);v1s(:,1);sas(:,1)]))];
  191. t = (param_trial.lims_trial - param_trial.lims_stim(1))/30;
  192. tLims = [t(1) tMax];
  193. fixPlot(hRaster,tLims,[0 193],'','channel',0:250:t(2),0:96:256) % 257 for 7a
  194. set(hRaster,'xtick',[]);
  195. axis(hRaster,'normal'); grid(hRaster,'off')
  197. % PSTH
  198. hold(hPsth,'on')
  199. allS = [v1s;v4s];
  200. % t = floor(min(allS(:,1))) :1: ceil(max(allS(:,1)));
  201. t = linspace(tLims(1),tLims(2),200);
  202. psths = zeros(192,length(t));
  203. for ii=1:192
  204. sp = allS(allS(:,2)==ii,1);
  206. psth = zeros(1,length(t));
  207. for s=1:length(sp)
  208. temp1 = getGaussian([1,sp(s),20],t);
  209. temp2 = getGaussian([1,sp(s),90],t);
  210. [~,idx] = max(temp1);
  211. temp = [temp1(1:idx) temp2(idx+1:end)];
  212. psth = psth + temp;
  213. end
  214. psth = psth-mean(psth(t<0));
  215. psth = psth * 1;
  216. % if ii<=96
  217. % col = cols(2,:);
  218. % else
  219. % col = cols(1,:);
  220. % end
  221. % plot(hPsth,t,psth,'color',col); hold on;
  222. psths(ii,:) = psth;
  223. end
  224. psth_v1 = mean(psths(1:96,:));
  225. psth_v4 = mean(psths(97:end,:));
  226. plot(hPsth,t,psth_v1,'color',cols(2,:),'linewidth',2);
  227. plot(hPsth,t,psth_v4,'color',cols(1,:),'linewidth',2);
  228. line(hPsth,[50 550],[0 0],'linewidth',5,'color','k')
  229. arrayfun(@(jj) line(hPsth,[tMarks(jj) tMarks(jj)],[-1 10],'linewidth',2,'color','k'),1:length(tMarks))
  230. fixPlot(hPsth,tLims,[-1 10],'time (ms)','response (a.u.)',0:250:max(t),0:96:256)
  231. axis(hPsth,'normal'); grid(hRaster,'off')
  232. % psths = flipud(psths);
  233. end

supp3_tuning.m at commit bcb6e62, under MIT · at the source

Overview

Authors: Ramanujan Srinath1, Martyna M Czarnik1, Marlene R Cohen1
ORCID iDs: Ramanujan Srinath, Marlene R Cohen
  1. Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL USA
Institutions: University of Chicago (United States)
Journal: Nature communications, volume 17, issue 1, article 7914
Dates: received 10 March 2025; accepted 12 June 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74818-y · PMID 42342672 · PMCID PMC13443561 · OpenAlex W7165729775
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), non-human primate (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Object vision, Perception, Neural decoding, Decision
MeSH: Generalization, Psychological*, Visual Cortex*, Visual Perception*, Animals, Eye Movements, Macaca mulatta, Male, Neurons, Photic Stimulation, Saccades (* major topic)
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NEI NIH HHS (R01 EY034723, R01 EY022930, K99 EY035362); U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) (R01EY034723, K99EY035362, R01EY022930, RF1NS121913); Schmidt Sciences AI in Science Postdoctoral Fellowship Gordon and Betty Moore Foundation; U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (R01EY022930, R01EY034723, RF1NS121913, K99EY035362); Simons Foundation (542961SPI); NINDS NIH HHS (RF1 NS121913)
Citations: cited by 2 papers (Europe PMC); 55 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.

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ramanujansrinath/flexigain

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bcb6e62cc85ca5d7f509283f1c75ca0e3d3e9d6f, 22 May 2026
Languages: MATLAB (31), Python (6)
Size: 94 files, 37 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (10 files), NumPy (5 files), Matplotlib (2 files), SciPy (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
39 files
At the source: github.com/ramanujansrinath/flexigain

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Read it in the paper: doi.org/10.1038/s41467-026-74818-y.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 10 MeSH terms, 6 funders, 55 references.

Cite

This paper

Srinath, R., Czarnik, M. M., & Cohen, M. R. (2026). Stable readout of visual representations mediates flexible generalization. Nature communications, 17(1), 7914. https://doi.org/10.1038/s41467-026-74818-y

BibTeX

@article{srinath2026stable,
author = {Srinath, Ramanujan and Czarnik, Martyna M and Cohen, Marlene R},
title = {{Stable readout of visual representations mediates flexible generalization}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7914},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74818-y},
url = {https://doi.org/10.1038/s41467-026-74818-y},
pmid = {42342672},
pmcid = {PMC13443561}
}

RIS

TY - JOUR
AU - Srinath, Ramanujan
AU - Czarnik, Martyna M
AU - Cohen, Marlene R
TI - Stable readout of visual representations mediates flexible generalization
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/24
VL - 17
IS - 1
SP - 7914
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74818-y
UR - https://doi.org/10.1038/s41467-026-74818-y
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74818-y",
"type": "article-journal",
"title": "Stable readout of visual representations mediates flexible generalization",
"container-title": "Nature communications",
"author": [
{
"family": "Srinath",
"given": "Ramanujan"
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{
"family": "Czarnik",
"given": "Martyna M"
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{
"family": "Cohen",
"given": "Marlene R"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7914",
"DOI": "10.1038/s41467-026-74818-y",
"PMID": "42342672",
"PMCID": "PMC13443561",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-74818-y",
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"issued": {
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[6] doi:10.1038/s41467-026-72146-9 [code]
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Journal: Nature communications
In common: SciPy, Matplotlib, NumPy, 5 references
[7] doi:10.1016/j.isci.2026.117459 [code]
Behavioral states affect eye position tuning in the parietal cortex of macaques.
Journal: iScience
In common: Statistics and Machine Learning Toolbox, non-human primate, 5 references
[8] doi:10.1371/journal.pcbi.1014162 [code]
Exploring neural manifolds across a wide range of intrinsic dimensions.
Journal: PLoS computational biology
In common: SciPy, Matplotlib, NumPy, 5 references
[9] doi:10.1038/s42003-026-10036-y
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Journal: Communications biology
In common: behavior only, non-human primate, 5 references
[10] doi:10.1038/s41467-026-76104-3 [code]
Sensorimotor remapping drives task specialization in prefrontal cortex.
Journal: Nature communications
In common: SciPy, Matplotlib, NumPy, non-human primate, 4 references

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