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Visual motion does not bias gravity-referenced vestibular coding in the primate cerebellar nodulus and uvula.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Data analysis › Modeling of dynamic changes. ↔ NodulusUvulaVisualVestibular.m, lines 1–74 · score 0.75 · upward translations, visual scene, anti preferred direction, tuning direction, spikes, rightward
  2. [2] § Methods › Data analysis › Modeling of dynamic changes. ↔ NodulusUvulaVisualVestibular.m, lines 1–74 · score 0.69 · upward translations, visual scene, tuning direction, transparent, rotates, visual motion

Paper

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The authors' code

MATLAB · 231 lines · 13 KB · CC-BY-4.0 · 2 matches

  1. % Computational model of visual-vestibular interaction in Purkinje cells of
  2. % the primate cerebellar nodulus and ventral uvula (NU), and the code that
  3. % generates panels B-D of Figure 4 of the paper cited below.
  4. %
  5. % -------------------------------------------------------------------------
  6. % CITATION
  7. % -------------------------------------------------------------------------
  8. % If you use this code, or any modified version of it, you must cite:
  9. %
  10. % Gomez LJ*, Mildren RL*, Karmali F, Cullen KE. Visual Motion Does Not Bias
  11. % Gravity-Referenced Vestibular Coding in the Primate Cerebellar Nodulus
  12. % and Uvula. PLOS Biology.
  13. % *co-first authors
  14. %
  15. % -------------------------------------------------------------------------
  16. % LICENSE
  17. % -------------------------------------------------------------------------
  18. % Copyright (c) 2026 Faisal Karmali
  19. %
  20. % This work is licensed under the Creative Commons Attribution 4.0
  21. % International License (CC BY 4.0).
  22. % Full terms: https://creativecommons.org/licenses/by/4.0/legalcode
  23. %
  24. % You are free to share and adapt this material for any purpose, including
  25. % commercially, provided that you give appropriate credit. Under section
  26. % 3(a) of the license, that credit must be retained in modified versions,
  27. % and you must indicate that changes were made. For this work, appropriate
  28. % credit means citing the paper above, retaining this notice, and stating
  29. % what you changed.
  30. %
  31. % MODEL
  32. %
  33. % Coordinate conventions:
  34. % +Y is rightward and +Z is upward; rightward and upward translations are
  35. % positive. 0 deg in the Y-Z plane points rightward and angles increase
  36. % counterclockwise. theta_vis carries the same sign as the visual scene
  37. % velocity that induces it.
  38. %
  39. % Published parameter values:
  40. % s_PD = 20 (spikes/s)/(m/s^2) preferred-direction sensitivity
  41. % s_APD = 10 (spikes/s)/(m/s^2) anti-preferred-direction sensitivity
  42. % theta_PD = -70, -35, 0, +35, +70 deg modelled tuning directions
  43. % visual velocity = -60, -30, 0, +30, +60 deg/s
  44. % theta_vis = -20, -10, 0, +10, +20 deg induced tuning rotation
  45. %
  46. % -------------------------------------------------------------------------
  47. % HOW TO RUN
  48. % -------------------------------------------------------------------------
  49. % Requires MATLAB R2020a or later (for polarplot). No toolboxes needed.
  50. %
  51. % Run to build the figure and write NU_rotation6.svg and NU_rotation6.tif.
  52. %
  53. % -------------------------------------------------------------------------
  54. % RELATIONSHIP TO THE PUBLISHED FIGURE
  55. % -------------------------------------------------------------------------
  56. % Two differences between this code's output and the figure as printed, both
  57. % cosmetic; no plotted value was altered.
  58. % 1. This code evaluates five tuning directions. Panels B-D as printed
  59. % show the middle three (-35, 0, +35 deg); the -70 and +70 deg rows
  60. % were cropped during layout. To reproduce the printed three, set
  61. % all_theta_PD=[-35 0 35] in the section below.
  62. % 2. Arrowheads were added to the polar vectors, and the rotated tuning
  63. % vectors were rendered semi-transparently, when the figure was laid
  64. % out for publication.
  65. %
  67. color_n60="#00AEEF";
  68. color_n30="#3BBA8F";
  69. color_p00="#000000";
  70. color_p30="#F9B721";
  71. color_p60="#ED1556";
  73. allvisvel=[-60 -30 0 30 60];
  74. allcolors={color_n60 color_n30 color_p00 color_p30 color_p60};
  77. %% Manuscript figure based on PD/APD: Expected responses based on trignometry
  78. % Assumes right and up are positive
  79. close all
  81. show_apd=0; % set to 1 to plot anti-preferred direction, and 0 to plot only preferred direction
  83. theta_vis_p60=20;
  84. theta_vis_p30=theta_vis_p60/2;
  85. theta_vis_p00=0;
  86. theta_vis_n60=-20;
  87. theta_vis_n30=theta_vis_n60/2;
  89. figure('DefaultTextFontName','Segoe UI');
  91. % The coordinate system is typical: +Y is to the right and +Z is up
  92. % Thus, the origin (0 deg) in the polar plots points right
  93. % +ve angles are counterclockwise
  95. all_theta_PD=-70:35:70; %-90:45:135;
  96. for ithetaPD=1:length(all_theta_PD)
  97. theta_PD=all_theta_PD(ithetaPD);
  99. s_PD=20;
  100. s_APD=10;
  102. [s_L_n60,s_R_n60,r_L_n60,r_R_n60]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis_n60);
  103. [s_L_n30,s_R_n30,r_L_n30,r_R_n30]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis_n30);
  104. [s_L_p00,s_R_p00,r_L_p00,r_R_p00]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis_p00);
  105. [s_L_p30,s_R_p30,r_L_p30,r_R_p30]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis_p30);
  106. [s_L_p60,s_R_p60,r_L_p60,r_R_p60]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis_p60);
  108. sim_response(ithetaPD).s_PD=s_PD;
  109. sim_response(ithetaPD).s_APD=s_APD;
  110. sim_response(ithetaPD).theta_PD=theta_PD;
  111. sim_response(ithetaPD).theta_vis_n60=theta_vis_n60;
  112. sim_response(ithetaPD).theta_vis_p00=theta_vis_p00;
  113. sim_response(ithetaPD).theta_vis_p60=theta_vis_p60;
  114. sim_response(ithetaPD).s_L_n60=s_L_n60;
  115. sim_response(ithetaPD).s_R_n60=s_R_n60;
  116. sim_response(ithetaPD).s_L_n30=s_L_n30;
  117. sim_response(ithetaPD).s_R_n30=s_R_n30;
  118. sim_response(ithetaPD).s_L_p00=s_L_p00;
  119. sim_response(ithetaPD).s_R_p00=s_R_p00;
  120. sim_response(ithetaPD).s_L_p30=s_L_p30;
  121. sim_response(ithetaPD).s_R_p30=s_R_p30;
  122. sim_response(ithetaPD).s_L_p60=s_L_p60;
  123. sim_response(ithetaPD).s_R_p60=s_R_p60;
  125. sim_response(ithetaPD).r_L_n60=r_L_n60;
  126. sim_response(ithetaPD).r_R_n60=r_R_n60;
  127. sim_response(ithetaPD).r_L_n30=r_L_n30;
  128. sim_response(ithetaPD).r_R_n30=r_R_n30;
  129. sim_response(ithetaPD).r_L_p00=r_L_p00;
  130. sim_response(ithetaPD).r_R_p00=r_R_p00;
  131. sim_response(ithetaPD).r_L_p30=r_L_p30;
  132. sim_response(ithetaPD).r_R_p30=r_R_p30;
  133. sim_response(ithetaPD).r_L_p60=r_L_p60;
  134. sim_response(ithetaPD).r_R_p60=r_R_p60;
  136. if (ithetaPD>0)
  137. subplot(5,3,3*(ithetaPD-1)+0+1);
  139. % Lines showing the PD and APD responses in the Y-Z plane
  140. if (show_apd)
  141. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p00+180)),[0 sim_response(ithetaPD).s_APD],'color',color_p00,'linestyle',':','linewidth',2);
  142. hold on;
  143. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p60+180)),[0 sim_response(ithetaPD).s_APD],'color',color_p60,'linestyle',':','linewidth',1);
  144. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_n60+180)),[0 sim_response(ithetaPD).s_APD],'color',color_n60,'linestyle',':','linewidth',1);
  145. end
  146. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p00+ 0)),[0 sim_response(ithetaPD).s_PD ],'color',color_p00,'linestyle','-','linewidth',3);
  147. hold on;
  148. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p60+ 0)),[0 sim_response(ithetaPD).s_PD ],'color',color_p60,'linestyle','-','linewidth',2);
  149. polarplot([1 1]*deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_n60+ 0)),[0 sim_response(ithetaPD).s_PD ],'color',color_n60,'linestyle','-','linewidth',2);
  151. % Lines connecting the diagonal Y-Z vector to the projection on the Y axis
  152. if (show_apd)
  153. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p00+180))],[sim_response(ithetaPD).r_L_p00 sim_response(ithetaPD).s_APD],'color',color_p00,'linestyle',':','linewidth',.5);
  154. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p60+180))],[sim_response(ithetaPD).r_L_p60 sim_response(ithetaPD).s_APD],'color',color_p60,'linestyle',':','linewidth',.5);
  155. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_n60+180))],[sim_response(ithetaPD).r_L_n60 sim_response(ithetaPD).s_APD],'color',color_n60,'linestyle',':','linewidth',.5);
  156. end
  157. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p00+ 0))],[sim_response(ithetaPD).r_R_p00 sim_response(ithetaPD).s_PD ],'color',color_p00,'linestyle','-','linewidth',.5);
  158. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_p60+ 0))],[sim_response(ithetaPD).r_R_p60 sim_response(ithetaPD).s_PD ],'color',color_p60,'linestyle','-','linewidth',.5);
  159. polarplot([0 deg2rad((sim_response(ithetaPD).theta_PD+sim_response(ithetaPD).theta_vis_n60+ 0))],[sim_response(ithetaPD).r_R_n60 sim_response(ithetaPD).s_PD ],'color',color_n60,'linestyle','-','linewidth',.5);
  161. % Lines showing the projection of responses on the Y axis
  162. if (show_apd)
  163. polarplot([0 0],[0 sim_response(ithetaPD).r_L_p00],'color',color_p00,'linestyle',':','linewidth',1);
  164. polarplot([0 0],[0 sim_response(ithetaPD).r_L_p60],'color',color_p60,'linestyle',':','linewidth',1);
  165. polarplot([0 0],[0 sim_response(ithetaPD).r_L_n60],'color',color_n60,'linestyle',':','linewidth',1);
  166. end
  167. polarplot([0 0],[0 sim_response(ithetaPD).r_R_p00],'color',color_p00,'linestyle','-','linewidth',3);
  168. polarplot([0 0],[0 sim_response(ithetaPD).r_R_p60],'color',color_p60,'linestyle','-','linewidth',2);
  169. polarplot([0 0],[0 sim_response(ithetaPD).r_R_n60],'color',color_n60,'linestyle','-','linewidth',2);
  171. set(gca,'RLim',[0 23]);
  172. set(gca,'RTick',[0 10 20]);
  173. set(gca,'RTickLabel',{'' '' ''});
  174. %set(gca,'ThetaTick',0:45:359);
  175. set(gca,'ThetaLim',[-180 180]);
  176. gca_InnerPosition=get(gca,'InnerPosition');
  177. gca_InnerPosition=[gca_InnerPosition(1)-.08 gca_InnerPosition(2)-.01 gca_InnerPosition(3)+.03 gca_InnerPosition(4)+.03];
  178. set(gca,'InnerPosition',gca_InnerPosition);
  180. text(.78*pi,40,['\theta_P_D=',num2str(sim_response(ithetaPD).theta_PD),'\circ'],'fontsize',11);
  182. all_axes={['Response modulation',10,'(sp/s)/(m/s^2)'] ['Sensitivity',10,'(sp/s)/(m/s^2)'] ['|Sensitivity|',10,'(sp/s)/(m/s^2)']};
  183. % the rows below correspond to each of the three axes above, with a pair of R and L for each
  184. all_y=[...
  185. [sim_response(ithetaPD).r_R_n60 sim_response(ithetaPD).r_R_n30 sim_response(ithetaPD).r_R_p00 sim_response(ithetaPD).r_R_p30 sim_response(ithetaPD).r_R_p60];
  186. [sim_response(ithetaPD).r_L_n60 sim_response(ithetaPD).r_L_n30 sim_response(ithetaPD).r_L_p00 sim_response(ithetaPD).r_L_p30 sim_response(ithetaPD).r_L_p60];
  187. [sim_response(ithetaPD).s_R_n60 sim_response(ithetaPD).s_R_n30 sim_response(ithetaPD).s_R_p00 sim_response(ithetaPD).s_R_p30 sim_response(ithetaPD).s_R_p60];
  188. [sim_response(ithetaPD).s_L_n60 sim_response(ithetaPD).s_L_n30 sim_response(ithetaPD).s_L_p00 sim_response(ithetaPD).s_L_p30 sim_response(ithetaPD).s_L_p60]
  189. abs([sim_response(ithetaPD).s_R_n60 sim_response(ithetaPD).s_R_n30 sim_response(ithetaPD).s_R_p00 sim_response(ithetaPD).s_R_p30 sim_response(ithetaPD).s_R_p60]);
  190. abs([sim_response(ithetaPD).s_L_n60 sim_response(ithetaPD).s_L_n30 sim_response(ithetaPD).s_L_p00 sim_response(ithetaPD).s_L_p30 sim_response(ithetaPD).s_L_p60]);...
  191. ];
  193. for icolumn=1:2
  194. subplot(5,3,3*(ithetaPD-1)+1+icolumn);
  195. plot(allvisvel,all_y(2*(icolumn-1)+2,:),'color',color_p00,'linestyle',':','linewidth',1);
  196. hold on
  197. plot(allvisvel,all_y(2*(icolumn-1)+1,:),'color',color_p00,'linestyle','-','linewidth',2);
  198. for ii=1:5
  199. plot(allvisvel(ii),all_y(2*(icolumn-1)+2,ii),'o','color',allcolors{ii},'linewidth',2);
  200. plot(allvisvel(ii),all_y(2*(icolumn-1)+1,ii),'o','color',allcolors{ii},'linewidth',2,'markerfacecolor',allcolors{ii});
  201. end
  202. ylim([-22 22]);
  203. xlim([-70 70]);
  204. xlabel('Visual velocity (\circ/s)');
  205. ylabel(all_axes{icolumn});
  206. set(gca,'box','off','XTick',[-60 0 60]);
  207. end
  209. end
  210. end
  211. set(gcf, 'Units','inches', 'Position', [.25 .25 7 12 ],'PaperPositionMode', 'manual','PaperUnits', 'inches','PaperPosition', [.25 .25 7 12]);
  212. print('-dsvg','-r1200','NU_rotation6.svg');
  213. print('-dtiff','-r1200','NU_rotation6.tif');
  215. %This function assumes that the preferred direction is always in the positive (right) direction
  216. %s_PD - sensitivity of neuron in the preferred axis (in the y-z plane)
  217. %s_APD - sensitivity of neuron in the anti-preferred axis (in the y-z plane)
  218. %theta_neuron - angle between preferred direction in the y-z plane and the y axis
  219. %theta_visual - rotation angle of neuronal response vector due to visual stimulus
  220. function [s_L,s_R,r_L,r_R]=predict_NU_responses_PD_APD(s_PD,s_APD,theta_PD,theta_vis)
  221. adjusted_PD=theta_PD+theta_vis;
  222. if (adjusted_PD>=-90 & adjusted_PD<=90)
  223. r_R=(s_PD * cosd ( (adjusted_PD + 0) ));
  224. r_L=(s_APD * cosd ( (adjusted_PD + 180) ));
  225. else
  226. r_R=(s_APD * cosd ( (adjusted_PD + 180) ));
  227. r_L=(s_PD * cosd ( (adjusted_PD + 0) ));
  228. end
  229. s_L=(r_L)/-1;
  230. s_R=(r_R)/+1;
  231. end

NodulusUvulaVisualVestibular.m, under CC-BY-4.0 · at the source

Overview

Authors: Lex J. Gómez1, Robyn L. Mildren1, Faisal Karmali2,3, Kathleen E. Cullen1,4,5,6
ORCID iDs: Lex J. Gómez, Faisal Karmali, Kathleen E. Cullen
  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
  2. Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
  3. Mass General Brigham, Massachusetts Eye and Ear, Boston, Massachusetts, United States of America
  4. Department of Otolaryngology, Johns Hopkins University, Baltimore, Maryland, United States of America
  5. Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland, United States of America
  6. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
Institutions: Johns Hopkins University (United States); Massachusetts Eye and Ear Infirmary (United States); Harvard University (United States); Mass General Brigham (United States)
Journal: PLoS biology, volume 24, issue 8, article e3003972
Dates: received 18 June 2026; accepted 11 August 2026; published online 31 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003972 · PMID 42672117 · PMCID PMC13577572 · OpenAlex W7203771400
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Cerebellum*, Motion Perception*, Vestibule, Labyrinth*, Animals, Gravitation, Macaca mulatta, Male, Orientation, Photic Stimulation, Purkinje Cells (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Vision, Psychology, Social Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Purkinje Cells, Cellular Neuroscience, Physical Sciences, Physics, Classical Mechanics, Motion, Neuronal Tuning, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Zoology, Acceleration, Velocity
Topic: Vestibular and auditory disorders (Neurology, Neuroscience), according to OpenAlex
Funding: National Institute on Deafness and Other Communication Disorders (R01-DC002390, R01-DC018061); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01-AG075532); Kavli Foundation (N/A)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Visual motion is known to influence perceptions of tilt, verticality, and translation, suggesting that optic flow is combined with vestibular cues to estimate orientation relative to gravity. The cerebellar nodulus and ventral uvula (NU) are a prime candidate to perform this computation because this region uniquely receives convergent semicircular canal, otolith, and proprioceptive inputs, and in non-primate species full-field visual motion robustly modulates NU activity. Here, we tested whether visual roll motion, known to bias perceived orientation relative to gravity, alters the internal gravity-referenced transformation used by NU neurons to encode vestibular self-motion. To test this, we recorded single-unit activity from NU Purkinje cells in rhesus macaques during whole-body translations in darkness, either without visual stimulation or after prolonged full-field optokinetic roll motion. We hypothesized that visual motion simulating head tilt would bias the NU’s internal gravity estimate, leading to altered translation-evoked responses. Contrary to this prediction, visual motion had no effect on either baseline firing rates or vestibular responses. Moreover, a computational model predicting visually induced shifts in neural tuning was not supported by the data. These results show that visual roll motion, although known to influence perceived orientation, does not bias gravity-referenced vestibular coding in the primate NU. This specialization may preserve a fast, body-anchored gravity estimate for postural and reflexive motor control, delegating visual–vestibular integration for perception to downstream circuits.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

figshare 32836397

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 7 files, 1 script
Software Heritage: not checked
Found in: “Data 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)
1 file
At the source: doi.org/10.6084/m9.figshare.32836397

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 2 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 dataset and custom code supporting this study are publicly available on Figshare at https://doi.org/10.6084/m9.figshare.32836397.

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, issue, pages, dates, 4 authors, 10 MeSH terms, 3 funders, 89 references.

Cite

This paper

Gómez, L. J., Mildren, R. L., Karmali, F., & Cullen, K. E. (2026). Visual motion does not bias gravity-referenced vestibular coding in the primate cerebellar nodulus and uvula. PLoS biology, 24(8), e3003972. https://doi.org/10.1371/journal.pbio.3003972

BibTeX

@article{gomez2026visual,
author = {Gómez, Lex J. and Mildren, Robyn L. and Karmali, Faisal and Cullen, Kathleen E.},
title = {{Visual motion does not bias gravity-referenced vestibular coding in the primate cerebellar nodulus and uvula}},
journal = {PLoS biology},
year = {2026},
month = aug,
volume = {24},
number = {8},
pages = {e3003972},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003972},
url = {https://doi.org/10.1371/journal.pbio.3003972},
pmid = {42672117},
pmcid = {PMC13577572}
}

RIS

TY - JOUR
AU - Gómez, Lex J.
AU - Mildren, Robyn L.
AU - Karmali, Faisal
AU - Cullen, Kathleen E.
TI - Visual motion does not bias gravity-referenced vestibular coding in the primate cerebellar nodulus and uvula
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/08/31
VL - 24
IS - 8
SP - e3003972
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003972
UR - https://doi.org/10.1371/journal.pbio.3003972
LA - en
ER -

CSL-JSON

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"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003972",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
31
]
]
}
}

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