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Divergent neural representations of space and task between physical and virtual navigation in macaques.

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The authors' code

Markdown · 43 lines · 3.2 KB · CC-BY-4.0

  1. # VR-RW
  2. Under VR and real-world environments, the differences of neuronal spatial and task representations in non-human primate's hippocampus and orbitofrontal cortex
  3. 猕猴海马及眶额皮层，在VR和RW两种环境中，空间与任务的神经表征差异
  4. # Participant
  5. Wenxin Yan; Dun Mao
  6. ION, Shanghai, China
  7. # Code description
  8. 1. **Behavior**
  9. - duration_fit.m: Number of trials to the plateau phase in the first block of each session and visualization.
  10. - duration_relative.m: Draw the learning curve
  11. - Occup_prop.m: Bar plot for the occupancy fraction between the near-target zone and the far-target zone.
  12. - PlotTraj.m: Draw heatmap for the occupancy of each block.
  13. 2. **Searching**
  14. - SV3d.m: 3d heatmap for gaze occupancy.
  15. - Searching.m: Plot curve for time of viewing landmarks when monkey approched the target zone.
  16. - Hd_cumulation.m: Plot curve for head rotation when monkey approched the target zone.
  17. - SvPlot.m: 3d scatter plot of fixation when monkey approched the target zone for each trial. And bar plot for fraction of time looking at screens during that trial.
  18. - SV2d.m: 2d heatmap for gaze occupancy.
  19. 3. **DiffContext**
  20. - GAMfitting_RW_SMT.m: Generate GAM fitting model for the smt in RW.
  21. - GAMfitting_VR_SMT.m: Generate GAM fitting model for the smt in VR.
  22. - ExtractBehavior_RW.m: Extract relevant behavior matrix for analysis in RW.
  23. - ExtractBehavior_VR.m: Extract relevant behavior matrix for analysis in VR.
  24. - GAM_2D_plot.m: Visualization of neuronal tuning to various spatial variables.
  25. - SICforSpatial.m: Spatial information of OFC and HPC in VR and RW environment.
  26. - TuningProp.m: Quantified the proportion of neurons significantly tuned to each spatial variable.
  27. - var_prop.m: Pie chart for the fraction of each spatial variables
  28. - allo_ego.m: Determine the type of neuron tuning -- pure-allo, pure-ego or mixed coding.
  29. - var_prop_change.m: Visulation of the change of neuronal encoding characteristics from VR to RW. Containing encoding fraction of each var, encoding fraction of allo var, single-var-tuning vs. mixed-var-tuning.
  30. - vr_arena_ratio.m: Match identical neurons between VR and RW, compare their firing rates.
  31. - SameNeu.m: Find identical neurons between VR and RW, and plot the mean waveform.
  32. - VR_RW_prop.m: For identical neurons, whether gain, lose, maintain or change their spatial representations from VR to RW.
  33. 4. **TaskState**
  34. - TaskTuning.m: Plot tuning curve according to relative-time task phases.
  35. - sequence.m: Heatmap for sequenced population vector of relative-time task phases and correlation of population vector between block 1 and block 2.
  36. - corr_Context.m: Population vector correlation between two blocks during the searching phase in VR and RW environment.
  37. - Bootstrap.m: Based on Bootstrap confidence interval statistical test for VR and RW correlation during the searching phase.
  38. - sequence_vr_rw.m: Heatmap for sequenced population vector of relative-time task phases and correlation of population vector between VR and RW. Only the sessions that had identical neurons were contained.
  39. - distribution.m: Peak shift for different blocks.
  40. 5. **MDS**
  41. - mds_state.m: Calculate RDM matrix and do MDS.
  42. - PCA.m: Do principal component analysis.
  43. - umap_plot.m:

README.md, under CC-BY-4.0 · at the source

Overview

Authors: Wenxin Yan1,2, Yan Huang1,2, Xuanzi Cao1, Dun Mao1,2
ORCID iDs: Wenxin Yan, Dun Mao
  1. Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
  2. University of Chinese Academy of Sciences, Beijing, China
Institutions: Chinese Academy of Sciences (China); Center for Excellence in Brain Science and Intelligence Technology (China); University of Chinese Academy of Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 5365
Dates: received 25 July 2025; accepted 7 April 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72141-0 · PMID 41997911 · PMCID PMC13276072 · OpenAlex W7154708371
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Spatial memory, Hippocampus
MeSH: Hippocampus*, Neurons*, Prefrontal Cortex*, Space Perception*, Spatial Navigation*, Virtual Reality*, Animals, Cognition, Macaca mulatta, Male, Spatial Memory (* 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) (32371076)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

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Repository

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Zenodo 15672309

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 8 files
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
1 file
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Read it in the paper: doi.org/10.1038/s41467-026-72141-0.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 11 MeSH terms, 1 funder, 57 references.

Cite

This paper

Yan, W., Huang, Y., Cao, X., & Mao, D. (2026). Divergent neural representations of space and task between physical and virtual navigation in macaques. Nature communications, 17(1), 5365. https://doi.org/10.1038/s41467-026-72141-0

BibTeX

@article{yan2026divergent,
author = {Yan, Wenxin and Huang, Yan and Cao, Xuanzi and Mao, Dun},
title = {{Divergent neural representations of space and task between physical and virtual navigation in macaques}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5365},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72141-0},
url = {https://doi.org/10.1038/s41467-026-72141-0},
pmid = {41997911},
pmcid = {PMC13276072}
}

RIS

TY - JOUR
AU - Yan, Wenxin
AU - Huang, Yan
AU - Cao, Xuanzi
AU - Mao, Dun
TI - Divergent neural representations of space and task between physical and virtual navigation in macaques
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/17
VL - 17
IS - 1
SP - 5365
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72141-0
UR - https://doi.org/10.1038/s41467-026-72141-0
LA - en
ER -

CSL-JSON

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