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Updating an allocentric goal from lateralised egocentric visual memories.

Code ↔ Paper

4 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 4 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Result › A closed-loop interaction between egocentric terrestrial and allocentric celestial guidance ↔ Fig4 Paths_Sun_Mirror/model_prediction/Run_CX_Processing_mirror.m, lines 1–141 · score 0.80 · Mirrored Sun, Sun rotation, motor noise, Real ant, turn angles, onset
  2. [2] § Result › A closed-loop interaction between egocentric terrestrial and allocentric celestial guidance ↔ Fig3 CX_model/CX_Agent_FB_bump.m, the whole file · a weak match · score 0.67 · motor noise, familiarity signals, bump, FB, activates, cell
  3. [3] § Result › The CX circuitry is suited to receive lateralised input to produce goal-oriented paths ↔ Fig3 CX_model/CX_Agent_FB_bump.m, the whole file · a weak match · score 0.57 · familiarity signal, visual memories, agent, decay, CX, steers
  4. [4] § Result › Guidance based on memorised views involves the celestial compass ↔ Fig4 Paths_Sun_Mirror/model_prediction/CX_Agent_Ant_mirrorsun.m, the whole file · a weak match · score 0.52 · heading direction, visual memories, sun, steering, CX, motor

Paper

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The authors' code

MATLAB · 109 lines · 3.4 KB · no license · 2 matches

  2. function [x,y,th,turn,err_dir,straightness] =CX_Agent_FB_bump(time, mem_bias, decay_CPU4, gain, motor_noise_deg, mem_noise_deg)
  4. %(x,y) are the agent coordinate
  5. %th is the current heading direction;
  7. % Basic parameters
  8. step_length = 1;
  9. max_angle = 180/180*pi; %boundary condition for the oscillation Careful below of the sign if not 180 and zero!
  10. min_angle = 0/180*pi;
  12. PB_thref = 0:pi/4:2*pi-0.1;
  13. bump_pwr = 4; % the higher the more the winner take all, the smaller the more linear
  14. % PB=((pi-abs(pi2pi(PB_thref - th))).^pwr) / pi.^pwr; % example
  16. mem_L= mem_bias/180*pi;
  17. mem_R= -mem_bias/180*pi;
  19. mem_noise=mem_noise_deg/180*pi;
  20. motor_noise=motor_noise_deg/180*pi;
  22. % Allocation variable cumulative
  23. turn=nan([1,time]);
  24. th=nan([1,time+1]);
  25. x=nan([1,time+1]);
  26. y=nan([1,time+1]);
  28. % Initial conditions
  29. x(1)=0;
  30. y(1)=0;
  31. th(1)=pi22pi(rand*2*pi); % th is the actual agent bearing
  32. HDB = zeros([1,8]);
  34. for i=1:time %i refer to time before movement.
  36. % Get EPG activation (0-1)
  37. EPG_raw =((pi-abs(pi2pi(PB_thref - th(i)))).^bump_pwr);
  38. EPG = (EPG_raw / max(EPG_raw) );
  40. %--------------- Update CPU4 memory -------------
  42. % Add noise to the direction of the visual memory
  43. mem_Ln = mem_L + normrnd(0,mem_noise);
  44. mem_Rn = mem_R + normrnd(0,mem_noise);
  45. % Get View Familiarities (0-1)
  46. Fam_L = ((pi - abs(pi2pi(mem_Ln - th(i)))) /pi)^2;
  47. Fam_R = ((pi - abs(pi2pi(mem_Rn - th(i)))) /pi)^2;
  48. % fam_R =0; % Covered eye = always max unfamiliair (pi)
  52. % Get PFN input. Here Familiarity signal activate a shifted activation in the FB (PFN cells)
  53. PFN_L_input = Fam_L * circshift(EPG,-1);%-1 put the bump 45° clockwise (left), so towards the forward direction (as Fam_L is when heading Right off the nest)
  54. PFN_R_input = Fam_R * circshift(EPG,+1);%+1
  56. % Updtate allocentric goal (HdB cells) by adding the PFN cell and its decay
  57. HDB = PFN_L_input + PFN_R_input + HDB - HDB*decay_CPU4;
  61. %--------------- Steering -------------
  63. % Get CPU1 activation
  65. CPU1_L = HDB - circshift(EPG,+1);
  66. CPU1_R = HDB - circshift(EPG,-1);
  68. CPU1_L(CPU1_L<0) = 0;
  69. CPU1_R(CPU1_R<0) = 0;
  73. % Get the turning rate (mean instead of sum to stay between 0-1)
  74. turn_r = sum(CPU1_L); % Note that a good left match (high CPU1_L) means turn right
  75. turn_l = sum(CPU1_R);
  77. % Will need to change when the oscillator comes into play
  78. turn(i) = turn_l - turn_r;
  80. angle_modul = turn(i)*gain ;
  81. % boundary
  82. if abs(angle_modul) < min_angle; angle_modul = min_angle * sign(angle_modul); end
  83. if abs(angle_modul) > max_angle; angle_modul = max_angle * sign(angle_modul); end
  88. th(i+1)= th(i)+ angle_modul; %set the new heading
  90. %add some motor noise (sensory noise could be also added)
  91. th(i+1) = th(i+1) + normrnd(0,motor_noise);
  93. % % calculate the stepsize
  94. stepsize = step_length;
  95. % stepsize = (step_length* (1-fwd_modul)) / nb_step;
  96. % if stepsize<0 ; stepsize=0;
  97. % end
  99. %--------move to time i+1------------
  101. [X, Y]=pol2cart(th(i+1) , stepsize); %move along this direction
  102. x(i+1)= x(i)+X;
  103. y(i+1)= y(i)+Y;
  105. end
  107. [th_end, r_end]=cart2pol(x(end),y(end));
  108. err_dir=abs(th_end);
  109. straightness = r_end/time;

CX_Agent_FB_bump.m at commit f251e17, no license · at the source

Overview

Authors: Antoine Wystrach1, Florent Le Moël1,2, Leo Clement1, Sebastian Schwarz1,3
ORCID iDs: Antoine Wystrach, Florent Le Moël
  1. Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université de Toulouse, CNRS, Toulouse, France
  2. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
  3. Department of Biology, University of Graz, Graz, Austria
Institutions: Centre National de la Recherche Scientifique (France); Centre de Recherches sur la Cognition Animale (France); Université de Toulouse (France); Centre de Biologie Intégrative de Toulouse (France); University of Edinburgh (United Kingdom); University of Graz (Austria)
Journal: Nature communications, volume 17, issue 1, article 3594
Dates: received 12 February 2025; accepted 3 December 2025; published online 6 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-025-67545-3 · PMID 41792124 · PMCID PMC13096645 · OpenAlex W7134050448
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), cognitive (subfield)
Keywords: Cognitive neuroscience, Animal behaviour
MeSH: Ants*, Memory*, Visual Perception*, Animals, Brain, Cues, Goals, Mushroom Bodies, Space Perception, Spatial Navigation (* major topic)
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101125881, 759817)
Citations: not cited yet (Europe PMC); 107 references in the paper

Abstract

Animals navigate by combining egocentric (viewpoint-dependent) and allocentric (world-referenced) spatial representations, yet how their brains achieve this integration remains unclear. Here we show how the brains of insect expert navigators, such as ants, accomplish this task. Field experiments reveal that ants recognise long-term egocentric visual memories – assumed to be encoded in the Mushroom Bodies – via a lateralized mechanism: instead of memorising views while facing their goal, ants store these memories by looking to the sides. Recognition signals inform whether to turn left or right, but do not directly drive motor responses. Instead, they are processed separately – presumably in the two brain hemispheres – and integrated to update a goal heading in an ancestral, central brain region –the central complex. This goal heading —now anchored in an allocentric frame—is then used with celestial compass cues for robust steering. Computational models based on insect neural circuits validate this two-stage process, demonstrating how noisy, viewpoint-dependent lateralized inputs are transformed into stable allocentric directional control. These findings reveal how compact brains leverage bilateral processing to combine spatial representations for visual navigation.

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 4 matches between paragraphs and lines of code.

antnavteam/lateralised_visual_memories

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f251e17ac9f4117f39df2a9af0660ce9e802f268, 6 November 2025
Languages: MATLAB (27), R (1)
Size: 38 files, 28 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (7 files), CircStat (2 files), ggplot2 (1 file), nlme (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
29 files
At the source: github.com/antnavteam/lateralised_visual_memories

Code availability

Codes to analyse data and run simulations have been deposited on GitHub: https://github.com/antnavteam/lateralised_visual_memories.

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

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;
  • 28 scripts, each with its path and the digest of its content;
  • 4 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 Statement

The data generated in this study have been deposited on GitHub: https://github.com/antnavteam/lateralised_visual_memories.

Codes to analyse data and run simulations have been deposited on GitHub: https://github.com/antnavteam/lateralised_visual_memories.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 10 MeSH terms, 1 funder, 93 references.

Cite

This paper

Wystrach, A., Le Moël, F., Clement, L., & Schwarz, S. (2026). Updating an allocentric goal from lateralised egocentric visual memories. Nature communications, 17(1), 3594. https://doi.org/10.1038/s41467-025-67545-3

BibTeX

@article{wystrach2026updating,
author = {Wystrach, Antoine and Le Moël, Florent and Clement, Leo and Schwarz, Sebastian},
title = {{Updating an allocentric goal from lateralised egocentric visual memories}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3594},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-025-67545-3},
url = {https://doi.org/10.1038/s41467-025-67545-3},
pmid = {41792124},
pmcid = {PMC13096645}
}

RIS

TY - JOUR
AU - Wystrach, Antoine
AU - Le Moël, Florent
AU - Clement, Leo
AU - Schwarz, Sebastian
TI - Updating an allocentric goal from lateralised egocentric visual memories
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/06
VL - 17
IS - 1
SP - 3594
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-025-67545-3
UR - https://doi.org/10.1038/s41467-025-67545-3
LA - en
ER -

CSL-JSON

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