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Scanning and active sampling behaviours emerge from conserved insect neural circuits

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and methods › CX steering component › Intrinsic LAL oscillator ↔ oscillator_agent_fwd_inhibition.m, lines 1 to 44 · score 0.76 · steady state, Neural activity, exhaustion rate, firing rate, Intrinsic, oscillator · Report a mismatch
  2. [2] § Materials and methods › CX steering component › Intrinsic LAL oscillator ↔ oscillator_agent_fwd_inhibition.m, lines 87 to 128 · score 0.75 · synaptic exhaustion, negative feedback, oscillatory activity, reciprocally inhibiting · Report a mismatch
  3. [3] § Materials and methods › Model parameters ↔ oscillator_agent_fwd_inhibition.m, lines 1 to 44 · score 0.74 · reciprocal inhibition strength, oscillatory cycle, Firing rates, Angular speed, Forward speed, neurons · Report a mismatch
  4. [4] § Materials and methods › CX steering component › Intrinsic LAL oscillator ↔ oscillator_agent_fwd_inhibition.m, lines 87 to 128 · score 0.72 · reciprocal inhibition strength, synaptic exhaustion, negative feedback, noise, oscillator · Report a mismatch
  5. [5] § Materials and methods › CX steering component › Motor output ↔ oscillator_agent_fwd_inhibition.m, lines 130 to 168 · score 0.64 · motor noise, motor gain, angular speed, fwd, forward speed, oscillator · Report a mismatch
  6. [6] § Results and discussion › The oscillator phase influences fixation duration ↔ Ant_scanning_analysis.m, lines 271 to 288 · score 0.59 · sequential phase information, binary reversal, fixation duration, interaction, model · Report a mismatch
  7. [7] § Materials and methods › CX steering component ↔ oscillator_agent_fwd_inhibition.m, lines 47 to 84 · score 0.56 · Protocerebral bridge, cells, HDB, bump, neurons, agent · Report a mismatch
  8. [8] § Materials and methods › CX steering component ↔ oscillator_agent_fwd_inhibition.m, lines 47 to 84 · score 0.53 · Protocerebral bridge, sector, PB, neural, inhibition, body · Report a mismatch
  9. [9] § Results and discussion › CX steering influences saccade magnitude ↔ Ant_scanning_analysis.m, lines 241 to 268 · score 0.50 · random error, saccade amplitude, LME, ant, model, scanning · Report a mismatch

Paper

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The authors' code

MATLAB · 306 lines · 11 KB · no license · 7 matches

  1. %% General explanation
  2. % This function models an intrinsic oscillator modulated by a CX ouptut,
  3. % The CX ouptut activity depend on the agent heading relative to its goal
  4. % heading (here constant). The oscillator left and right neuron activity
  5. % determines the agent angular speed (L-R) and forward speed (L+R) signals (+noise).
  6. % Angular speed is also linearly impeded by forward speed (the quicker the agent goes forward) the harder it is to turn.
  7. % This produce movements
  8. % At a given time (fwd_speed_inhibition_begining) an external inhibition of the forward speed can be applied
  9. % This inhibition last for a random duration (ends following a poisson statists).
  10. % If fwd_speed_inhibition is set at 0, the agent fully stop (assuming CPG creating movement is stopped)
  11. % The stopped agent needs to cumulate sufficient angular drive to start the CPG again and effect a
  12. % turn (resulting in saccads) unitl the inhibition period ceased.
  13. % If fwd_speed_inhibition is set >0, the agent forward speed is inhibited
  14. % during the inhibition time peridod, but not halted, so guidance continues as usual
  16. function [current_fixation_nb, body_orientation, Fixation_Duration,...
  17. X,Y,TH,CX_out,R,L, bearing_to_goal, ang_speed, fwd_speed]= oscillator_agent_fwd_inhibition (time, CX_gain, fwd_speed_inhibition_begining, stop_inhib_proba, fwd_speed_inhibition, myrmecia, display)
  21. % Agent parameter
  23. motor_gain_ang = 0.03; %0.03 (from neural activity to angle in Radian, act as multiplicator, the higher, the more zizaggy
  24. motor_noise = 0.1; %random angle added at each time step on both angular and forward speed
  25. CPG_ang_threshold = 2;% angular drive needed to restart the CPG movement (only used during true scan, fwd_speed =0;
  27. %desert ant forward control
  28. oscil_ctrl_fwd = 0.5;% (from neural activity to forward speed, act as power, so it increase forward variation acrooss the oscillatory cycle % 1.5 for myrmecia
  29. motor_gain_fwd = 1;
  31. %myrmecia ant forward control
  32. if myrmecia
  33. oscil_ctrl_fwd = 1.5;% (from neural activity to forward speed, act as power, so it increase forward variation acrooss the oscillatory cycle % 1.5 for myrmecia
  34. motor_gain_fwd = 0.2;
  35. end
  38. % Internal Oscillator parameters
  40. limit = 2; %max firing rate of neurons
  41. steady_state = 0.5;
  42. exhaust_rate = 0.01;
  43. reciprocal_inhibition_strength = 0.1;
  44. noise = 0.05; %will be normally distributed around 0;
  47. % CX parameters
  48. PB_thref = 0:pi/4:2*pi-0.1; %Protocerbral bridge sectors angle
  49. bump_pwr = 3; % encode neural bump width
  52. % Allocation variable cumulative
  53. [TH,X,Y,ang_speed, fwd_speed,R,L,R_NF,L_NF, CX_out,cumul_drive] = deal(nan([1,time]));
  55. %Allocation variable for scan
  56. [body_orientation, Fixation_Duration, current_fixation_nb, inhibition_time] =deal([]); % length = nb of fixation, cannot be known in advance.
  57. fix_nb = 1; % Register fix_nb
  58. frame_begining_fix = fwd_speed_inhibition_begining ; %Register the time of begining of each fixation (the first one start with the scan)
  60. %initial conditions CX
  61. th_goal = 0;
  62. HDB = ((abs(pi2pi(PB_thref - th_goal))).^bump_pwr) / pi.^bump_pwr; %hdelta cells represent the goal representation, here is assumed to be fixed in one direction
  63. % CX_out_R(1) = 0;
  64. % CX_out_L(1) = 0;
  67. % initial conditions oscillator
  68. R(1) = 1; % oscillator neuron firing rate
  69. L(1) = 0;
  70. R_NF(1) = 0; % internal exhaustion, negative feedback of neuron
  71. L_NF(1) = 0;
  73. % initial condition agent
  74. [X(1),Y(1)] = deal(0,0);
  75. TH(1) = 0;%(rand*2*pi)-pi;
  76. ang_speed(1) = 0;
  77. fwd_speed(1) = 0;
  78. cumul_drive(1) =0;
  79. speed_inhibition_time = 0;
  81. for i = 2:time
  82. if i == fwd_speed_inhibition_begining % then the inhibition on forward speed starts
  83. speed_inhibition_time=1;
  84. end
  87. % Update CX activity
  88. EPG =((pi-abs(pi2pi(PB_thref - TH(i-1)))).^bump_pwr) / pi.^bump_pwr; % EPG represent current heading bump
  90. PFL_L = HDB - circshift(EPG,+1); % PFL compare goal and current heading (2 version shifted by 45°)
  91. PFL_R = HDB - circshift(EPG,-1);
  92. PFL_L(PFL_L<0) = 0;
  93. PFL_R(PFL_R<0) = 0;
  95. CX_out_R = sum(PFL_R); % Get the output (mean instead of sum to stay between 0-1)
  96. CX_out_L = sum(PFL_L); % Note that a good left match (high CPU1_L) means turn right
  97. CX_out(i) = CX_out_R - CX_out_L;
  100. % Update oscillator activity (Negative feedback and lateral inhibition)
  101. R(i) = R(i-1) - R_NF(i-1) - (L(i-1)*reciprocal_inhibition_strength) + (noise*randn) ;
  102. L(i) = L(i-1) - L_NF(i-1) - (R(i-1)*reciprocal_inhibition_strength) + (noise*randn) ;
  105. % ----Add Input from CX output (Here each side inhibit the other, so winner
  107. % winner take all in CX before modulating the oscillator
  108. if CX_out(i)>0
  109. R(i) = R(i) + CX_out(i)*CX_gain ;
  110. else
  111. L(i) = L(i) - CX_out(i)*CX_gain ;
  112. end
  114. % % Both excite ipsi and inhibit contra
  115. % R(i) = R(i) + CX_out(i)*CX_gain ;
  116. % L(i) = L(i) - CX_out(i)*CX_gain ;
  119. % update negative feedback (synaptic exhaustion)
  120. R_NF(i) = R_NF(i-1)*(1-exhaust_rate) + (R(i) - (R_NF(i-1) + steady_state))*exhaust_rate;
  121. L_NF(i) = L_NF(i-1)*(1-exhaust_rate) + (L(i) - (L_NF(i-1) + steady_state))*exhaust_rate;
  124. %lower limit is zero
  125. if R(i) < 0; R(i) = 0; end
  126. if R(i) > limit; R(i) = limit; end
  127. if L(i) < 0; L(i) = 0; end
  128. if L(i) > limit; L(i) = limit; end
  130. %------------- move agent----------------------
  132. if speed_inhibition_time == 1 %---------------inhibition period ------------------------
  133. inhibition_time = [inhibition_time, i]; %register scanning's frames
  135. if fwd_speed_inhibition == 0 %------------------------- Full forward speed inhibition, CPG broken (proper scan)
  136. fwd_speed(i) = 0;
  138. if abs(cumul_drive(i-1)) <= CPG_ang_threshold + (noise*10*randn) % Stay fixed, but acumulate angular drive untiles it pass the threshold
  140. ang_speed(i) = 0;
  141. cumul_drive(i) = cumul_drive(i-1) + (L(i)-R(i)); %cumulate angular
  143. else % then end of fixation ; will display a saccad
  145. % get previous fixation data
  146. current_fixation_nb = [current_fixation_nb ; fix_nb] ;
  147. body_orientation = [body_orientation ; TH(i-1)];
  148. Fixation_Duration = [Fixation_Duration ; (i-frame_begining_fix)];
  150. % increament counter for next fixation
  151. fix_nb = fix_nb +1;
  152. frame_begining_fix = i;
  154. % calculate saccade
  155. ang_speed_raw = cumul_drive(i-1) + (L(i)-R(i)) ;
  156. ang_speed(i) = ang_speed_raw * motor_gain_ang * (1/(fwd_speed(i) + 0.15)) +(motor_noise*randn) ;
  157. cumul_drive(i)=0; %release the angular drive
  159. end
  161. else %------------------------------ --------non-full inhibition on forward speed, CPG not broken so angular speed proceed as normal,
  162. fwd_speed(i) = fwd_speed_inhibition + (motor_noise*randn);
  164. ang_speed_raw = L(i)-R(i); % usual angular speed control
  165. ang_speed(i) = ang_speed_raw * motor_gain_ang * (1/(fwd_speed(i) + 0.15)) +(motor_noise*randn) ;
  166. cumul_drive(i) = 0;
  168. end
  170. %Roll a dice to give a chance to end the forward speed inhibition
  171. dice = rand(1,1);
  172. if dice < stop_inhib_proba
  173. speed_inhibition_time = 0;
  174. end
  177. else % --------------------------------------------normal regime (no forward speed inhibition)
  179. fwd_speed_raw = L(i)+R(i);
  180. fwd_speed(i) = ((fwd_speed_raw .^ oscil_ctrl_fwd)+(motor_noise*randn))* motor_gain_fwd;
  182. ang_speed_raw = L(i)-R(i);
  183. ang_speed(i) = ang_speed_raw * motor_gain_ang * (1/(fwd_speed(i) + 0.15)) +(motor_noise*randn) ;
  184. cumul_drive(i) = 0;
  187. end
  188. TH(i) = TH(i-1) + ang_speed(i);
  189. [x,y] = pol2cart(TH(i),fwd_speed(i));
  190. X(i) = X(i-1) + x;
  191. Y(i) = Y(i-1) + y;
  194. end
  197. bearing_to_goal = pi2pi(TH - th_goal);
  198. body_orientation = pi2pi(body_orientation);
  201. %-------------------------Plot figures
  203. if display
  204. disp('Display is ON');
  206. cut = time/20; % Remove the begining for oscillator initialisation (unusually high values) does not overwhelm the color bar;
  208. figure()
  209. subplot(5,2,[1 3 5 7 9]);
  210. % color_line(X(cut:end),Y(cut:end),fwd_speed(cut:end),'lineWidth',2); hold on;
  211. colormap parula
  212. queue_length = -(fwd_speed(cut:end)-max(fwd_speed(cut:end)))*5; %so the saller the value, the bigger the head arrow
  213. color = fwd_speed(cut:end)+1;
  214. point_jump = 1;
  215. plot_angle_path (X(cut:end)',Y(cut:end)',TH(cut:end)',queue_length'+0.05,point_jump,2,color'); hold on
  216. axis equal
  217. text(0,0,'Start'); hold on
  218. scatter(0,0,'.','k');
  219. colorbar
  221. subplot(5,2,2);
  222. plot([0 time], [0 0], '--k'); hold on;
  223. for t = 2:time-1
  224. if CX_out(t) > 0
  225. color = 'b';
  226. elseif CX_out(t) <= 0
  227. color = 'r';
  228. end
  229. plot([t t+1], [CX_out(t) CX_out(t+1)], 'Color', color);
  230. end
  232. hCX = plot(nan, nan, 'ko', 'MarkerFaceColor', 'k');
  233. xlim([0,time]);
  234. title('Opponent CX output L(red), R(blue)');
  236. subplot(5,2,4);
  237. plot([R;L]');
  238. xlim([0,time+1]);
  239. title ('LAL neurons')
  241. subplot(5,2,6);
  242. scatter([1:length(TH)], bearing_to_goal, '.'); hold on
  243. plot([0,time+1],[0,0],'k--')
  244. ylim ([-3.15, 3.15])
  245. xlim([0,time+1]);
  246. title ('bearing to goal (0=goal direction)')
  248. subplot(5,2,8);
  249. plot(ang_speed,'k'); hold on,
  250. plot([0,time+1],[0,0],'k--')
  251. xlim([0,time+1]);
  252. title('ang speed (L - R)')
  254. subplot(5,2,10);
  255. plot(fwd_speed,'k'); hold on,
  256. xlim([0,time+1]);
  257. title('fwd speed (L + R)')
  260. %------------------ Figure focusing on the window of time during the forward speed inhibition -----
  262. if fix_nb>1 % if not, there was only one fixation.
  264. scan_duration = length(inhibition_time);
  266. figure()
  267. subplot(2,2,[1 2]);
  268. queue_length = 0.8;
  269. if length(Fixation_Duration) > 1
  270. color = Fixation_Duration;
  271. color = (color - min(color)) ; %normalise
  272. color = color/max(color);%normalise
  273. else
  274. color ='k';
  275. end
  276. point_jump = 1;
  277. plot_angle_path (current_fixation_nb,zeros([length(current_fixation_nb),1]),body_orientation,queue_length,point_jump,1,color); hold on
  278. xlim([0,length(current_fixation_nb)+1])
  279. axis equal;
  280. xlabel('fixation nb');
  281. colorbar;
  282. title('scan (color = fix duration)');
  284. subplot(2,2,3);
  285. color_line(inhibition_time - inhibition_time(1), cumul_drive(inhibition_time),inhibition_time); hold on;
  286. line([0, scan_duration-1],[CPG_ang_threshold, CPG_ang_threshold],'color','k');hold on;
  287. line([0, scan_duration-1],[-CPG_ang_threshold, -CPG_ang_threshold],'color', 'k');
  288. xlim([0, scan_duration-1]);
  289. xlabel('scan duration');
  290. title('cumul drive');
  292. subplot(2,2,4);
  293. color_line(inhibition_time - inhibition_time(1), ang_speed(inhibition_time),inhibition_time); hold on;
  294. plot([0, scan_duration-1],[0,0],'k--');
  295. xlim([0, scan_duration-1]);
  296. xlabel('scan duration');
  297. title('ang speed (L - R)');
  299. else
  300. warning('There has been only one fixation and thus no saccad (the cumul_drive didn''t reach the threshold before end of scan)')
  301. end
  303. end
  306. end

oscillator_agent_fwd_inhibition.m at commit e37c3fd, no license · at the source

Overview

Authors: Cody A Freas1,2, Antoine Wystrach1
  1. Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), CNRS, University of Toulouse, Toulouse, France
  2. School of Natural Sciences, Macquarie University, Sydney, Australia
Journal: eLife, volume 15, article RP110165
Dates: published online 6 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13641763
Status: code verified (how the code is found and verified)
Categories: other (organism), systems (subfield)
Methods: Statistics, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Other
Citations: not cited yet (Europe PMC); 133 references in the paper

Abstract

Navigating insects often pause and rotate to sample their surroundings, behaviours termed scanning. These and other active sampling behaviours embody navigational uncertainty and are key for spatial learning, yet their neural basis remains unclear and existing models impose scanning behaviours rather than explaining its emergence. Here, we show that desert ants’ scanning dynamics can emerge spontaneously from the same conserved neural circuits used for goal-directed navigation, without requiring a specialised scanning module. We built a biologically grounded model combining central complex steering and lateral accessory lobe oscillators and added a downstream stochastic inhibition of forward speed. This minimal system produced diverse, realistic scan dynamics; saccades, fixations, and reversals, whose features were qualitatively compared to high-speed video recordings of Melophorus bagoti scanning. Detailed analysis of these natural scans confirmed model predictions, including how scan structure depends on oscillator phase, goal-heading deviation, and navigational uncertainty. Furthermore, the model reveals that simple modulation of forward speed unifies a broad range of behaviours across ant species, from dashes to smooth oscillatory trajectories to pirouettes and voltes. Crucially, this model suggests a distributed control principle where forward speed acts as a single adjustable parameter, for both individuals and through evolution, to regulate the balance between goal-driven exploitation and information-seeking exploration.

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

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antnavteam/CX_oscillator_scans

License: none: the authors keep all their rights
State: the link answers, verified on 7 October 2026
Evidence: files inventoried
Commit: e37c3fdeb6652679eb1254b6d3b6c376b9f5d590, 7 October 2025
Languages: MATLAB (4)
Size: 5 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 7 October 2026: the link answers
  • 7 October 2026: the link answers
4 files
At the source: github.com/antnavteam/CX_oscillator_scans

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data availability

All data, documentation, and code are made available online at https://github.com/antnavteam/CX_oscillator_scans (copy archived at antnavteam, 2026).

The following previously published dataset was used:

Deeti S, Cheng K, Graham P. 2023. Scan Data. Open Science Framework. e7qag

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Recorded: type, language, journal, volume, pages, dates, 2 authors, 1 keyword, 127 references.

Cite

This paper

Freas, C. A., & Wystrach, A. (2026). Scanning and active sampling behaviours emerge from conserved insect neural circuits. eLife, 15, RP110165.

BibTeX

@article{freas2026scanning,
author = {Freas, Cody A and Wystrach, Antoine},
title = {{Scanning and active sampling behaviours emerge from conserved insect neural circuits}},
journal = {eLife},
year = {2026},
month = oct,
volume = {15},
pages = {RP110165},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
pmcid = {PMC13641763}
}

RIS

TY - JOUR
AU - Freas, Cody A
AU - Wystrach, Antoine
TI - Scanning and active sampling behaviours emerge from conserved insect neural circuits
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/10/07
VL - 15
SP - RP110165
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
LA - en
ER -

CSL-JSON

{
"id": "pmcid:PMC13641763",
"type": "article-journal",
"title": "Scanning and active sampling behaviours emerge from conserved insect neural circuits",
"container-title": "eLife",
"author": [
{
"family": "Freas",
"given": "Cody A"
},
{
"family": "Wystrach",
"given": "Antoine"
}
],
"container-title-short": "eLife",
"volume": "15",
"page": "RP110165",
"PMCID": "PMC13641763",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
7
]
]
}
}

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