OSCR

Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice.

Code ↔ Paper

16 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 16 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Pretraining surgeries › Surgery 1: intrinsic optical imaging ↔ HeadFixation/BarrelPiezoCamera.m, lines 1–98 · score 0.94 · Bpod State Machine, high power LED, MV1 D1024E, whisker stimulation, piezo, camera
  2. [2] § Materials and Methods › Pretraining surgeries › Surgery 1: intrinsic optical imaging ↔ HeadFixation/BarrelAveraging.m, the whole file · a weak match · score 0.89 · high power LED, blood vessels, anatomical reference, snapshot, curvature, illumination
  3. [3] § Materials and Methods › Whisker stimulation ↔ HeadFixation/PoleLocation.m, lines 1–91 · score 0.87 · Bpod State Machine, whisker imaging, head fixation, pole, Zaber, actuator
  4. [4] § Materials and Methods › Data analysis › State prediction based on nonperformance variables ↔ LearningMotivationStudy/LearnMotivStudy_StateClassif.m, the whole file · a weak match · score 0.84 · Classification Learner, template tree, learning cycles, decision tree, bagged, fitcensemble
  5. [5] § Materials and Methods › Head fixation, wheel running, and licking ↔ HeadFixation/PoleLocation.m, lines 1–91 · score 0.79 · Bpod Console, head fixation, daily sessions, Sanworks, interface, delivery
  6. [6] § Materials and Methods › Pretraining surgeries › Surgery 1: intrinsic optical imaging ↔ HeadFixation/BarrelAveraging.m, the whole file · a weak match · score 0.76 · red illuminated frames, blood vessel, snapshots, pixels, stimulation, interval
  7. [7] § Materials and Methods › Whisker stimulation ↔ HeadFixation/PassivePiezo.m, lines 1–100 · score 0.74 · Bpod State Machine, whisker stimulator, head fixation, passively, commands
  8. [8] § Materials and Methods › Pretraining surgeries › Surgery 1: intrinsic optical imaging ↔ HeadFixation/BarrelPiezoCamera.m, lines 1–98 · score 0.73 · skull transparency, S1 barrel, Intrinsic, anesthetized, glass, stimulation
  9. [9] § Materials and Methods › Data analysis › State prediction based on nonperformance variables ↔ LearningMotivationStudy/LearnMotivStudy_StateClassif.m, the whole file · a weak match · score 0.72 · vice versa, categorical vector, randomize, chronological, split, blocks
  10. [10] § Materials and Methods › Electrophysiology, data integration, and somatotopy check ↔ HeadFixation/PassivePiezo.m, lines 1–100 · score 0.69 · whisker stimulation, head fixation, RHD2000, Intan, interface, Bpod
  11. [11] § Materials and Methods › Data analysis › Summary values per session ↔ HumanREMinfraslow/humanREMinfraslow_SpectrPhaseQuantif.m, lines 1–82 · score 0.63 · power spectral density, PSD, pwelch, spaced, epoch, window
  12. [12] § Materials and Methods › Pretraining surgeries › Surgery 2: imaging-guided silicon probe implantation ↔ General/channelReMapping.m, the whole file · a weak match · score 0.60 · NeuroNexus, linear shank, Intan, probe, electrode
  13. [13] § Materials and Methods › Data analysis › Electrophysiologic signs of learning within states and trial outcomes ↔ HumanREMinfraslow/humanREMinfraslow_BetaPowerFluct.m, the whole file · a weak match · score 0.59 · temporal smoothing, frequency bins, spectrogram, spaced, sum, power
  14. [14] § Materials and Methods › Go/no-go training ↔ HeadFixation/PoleLocation.m, lines 525–639 · score 0.56 · light cue, onset, rewarded, water, alarm, hit
  15. [15] § Materials and Methods › Data analysis › Summary values per session ↔ REMinfraslow/Preproc/REM_DLCcoordsSingleEpoch.m, the whole file · a weak match · score 0.54 · video frames, DeepLabCut, pixel
  16. [16] § Results › Experimental design, summary of dataset, and study objects ↔ HeadFixation/PiezoDiscrimination.m, lines 248–337 · score 0.54 · correct rejection, go trials, discriminate, alarm, hit, head

Paper

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The authors' code

MATLAB · 639 lines · 24 KB · no license · 3 matches

  1. function PoleLocation
  3. % Trains head-fixed mice to discriminate pole locations through whisking.
  4. % The user is prompted to select one of two phases before each session.
  5. % Before starting this training, mice should be pre-trained for water
  6. % consumption at the lickometer.
  7. %
  8. % - Phase 1 (string 't'): two-location discrimination training (usually
  9. % 5-10 daily sessions of ~60 min).
  10. %
  11. % - Phase 2 (string 'm'): multiple location test (can be made just once or
  12. % twice, during post-training neurophysiological recordings).
  13. %
  14. %
  15. %
  16. % NECESSARY HARDWARE ______________________________________________________
  17. % - Bpod State Machine combined with Bpod Analog Output Module: reads task
  18. % parameters, controls trial loops, sends commands to Zaber actuators,
  19. % gets mouse responses at the lickometer (beam breaks), activates water
  20. % valve for rewards (or punishment time outs), provides timestamps to
  21. % Intan, displays behavioral performance during the session, and saves
  22. % trial outcomes.
  23. %
  24. % - Zaber X-MCB2: responds to inputs from both Bpod devices through
  25. % triggers configured by another script in C# (PoleLocTriggers.cs, also
  26. % available in this repository).
  27. %
  28. %
  29. %
  30. % OPTIONAL HARDWARE _______________________________________________________
  31. % - Intan RHD2000: acquires digital and analog events to be used as
  32. % timestamps during analysis.
  33. %
  34. % - Whisker imaging camera (Mikrotron, EoSens) and frame grabber
  35. % (Teledyne Dalsa Xtium): not described here.
  36. %
  37. % - Pupil imaging camera (Flea3) and PCIe card: not described here.
  38. %
  39. %
  40. %
  41. % TASK DESIGN (GO/NO-GO) __________________________________________________
  42. % - Inspired mostly by: - O'Connor et al. (2010) - J Neurosci 30,
  43. % - Schriver et al. (2018) - J Neurophysiol 120, and
  44. % - lab discussions
  45. %
  46. % - Whisker (pole) stimulation -> response window -> inter-trial interval
  47. %
  48. % - Up to five pole locations along the whisker pad, named according to the
  49. % anterior-posterior axis as below:
  50. % - PP: extreme posterior (both 't' and 'm' phases)
  51. % - P : intermediate posterior (phase 'm' only)
  52. % - H : halfway (phase 'm' only)
  53. % - A : intermediate anterior (phase 'm' only)
  54. % - AA: extreme anterior (both 't' and 'm' phases)
  55. %
  56. % - Locations PP and P: go trials
  57. % - Trial outcomes: hit (lick) or miss (no lick)
  58. % - Hit: water is delivered
  59. % - Miss: nothing happens (inter-trial interval)
  60. %
  61. % - Locations AA and A: no-go trials
  62. % - Trial outcomes: false alarm (lick) or corr rejection (no lick)
  63. % - False alarm: punishment (light-cued time out)
  64. % - Corr rejection: nothing happens (punishment is avoided)
  65. %
  66. % - Halfway: either go or no-go randomly (50% chance)
  67. %
  68. % - Locations vary at constant distances in the rostral-caudal direction.
  69. % See PoleLocTriggers.cs for distances along the whisker pad.
  70. %
  71. % - The multiple location test is hypothesized to yield sigmoid
  72. % psychometric fits.
  73. %
  74. % See the second section of this script ("Sets parameters") for stimulus
  75. % durations, trial length, etc.
  76. %
  77. %
  78. %
  79. % USAGE ___________________________________________________________________
  80. % Step 1: Load the Zaber Console software, open the X-MCB2 device, and run
  81. % PoleLocTriggers.cs. Check if "completed" is displayed in the Script
  82. % Output Area. Still in the Zaber Console, go to the "Simple" tab, and set
  83. % axis 1 position to zero, and axis 2 to the halfway (see
  84. % PoleLocTriggers.cs for the halfway position in microsteps).
  85. %
  86. % Step 2: Go back to Matlab, enter "Bpod" in the Command Window, and wait
  87. % until the Bpod Console is ready. Then enter "PoleLocation", and respond
  88. % to the prompt(s). With appropriate cabling, digital and analog events
  89. % should be visible in the Intan RHD interface.
  90. %
  91. % LSBuenoJr and MXDing, with inputs from the Sanworks Support Forum _______
  95. %% Sets base path, asks user about saving a previous session (if present in
  96. % the working directory), and asks user to specify the training phase.
  97. global BpodSystem;
  98. basepath = cd;
  100. if ~isempty(BpodSystem.Data) || exist('SessionData.mat') %#ok<EXIST>
  101. prompt = 'Save previous session? (y/n)';
  102. prompt = input(prompt,'s');
  103. if strcmp(prompt,'y')
  104. SessionData = BpodSystem.Data;
  105. save(fullfile(basepath,...
  106. 'PoleLocPreviousSession.mat'),'SessionData','-v6');
  107. end
  108. BpodSystem.Data = [];
  109. end
  111. prompt = ...
  112. 'Two locations (t), or Multiple locations (m)? ';
  113. prompt = input(prompt,'s');
  115. %% Configures square pulses to be sent from the Analog Output Module to
  116. % Zaber. They will trigger five different pole location stimuli. This
  117. % had to be done given the limited number of output channels offered by the
  118. % Bpod State Machine.
  119. W = BpodWavePlayer('COM6'); % If not this communication port, go to:
  120. % Control Panel -> Devices and Printers, then
  121. % left-click X-MCB2 to set properties.
  122. W.TriggerMode = 'Normal';
  123. W.TriggerProfileEnable = 'On';
  124. W.TriggerProfiles(1,1:5) = [1 4 4 4 4]; % Out of BNC #1 (Location PP)
  125. W.TriggerProfiles(2,1:5) = [4 2 4 4 4]; % ... #2 (Location P)
  126. W.TriggerProfiles(3,1:5) = [4 4 2 4 4]; % ... #3 (Location A)
  127. W.TriggerProfiles(4,1:5) = [4 4 4 1 4]; % ... #4 (Location AA)
  128. W.TriggerProfiles(5,1:5) = [4 4 4 4 3]; % ... #5 (Halfway location)
  130. PoleMovement = 0.8; % Period it takes for the pole to reach the whisker
  131. % level. This will be added to a 0.1 s trial onset
  132. % digital event for a total of 0.9 s (see the
  133. % first use of Bpod AddState function, in the last
  134. % section of this script). Durations were defined by
  135. % high-speed videography of pole movements.
  137. W.loadWaveform(1,...
  138. ones(1,W.SamplingRate*PoleMovement)*3); % Locations PP and AA; no
  139. % latency before pole movement
  140. % 3 volts
  142. W.loadWaveform(2,...
  143. [ones(1,W.SamplingRate*0.1)*0.5 ... % Latency of 0.1 s, 3 volts
  144. ones(1,W.SamplingRate*(PoleMovement-0.1))*3]); % Locations P and A
  146. W.loadWaveform(3,...
  147. [ones(1,W.SamplingRate*0.3)*0.5 ... % Latency of 0.3 s, 3 volts
  148. ones(1,W.SamplingRate*(PoleMovement+1.5))*3]); % Halfway location
  150. W.loadWaveform(4,...
  151. zeros(1,W.SamplingRate*PoleMovement)); % Nothing, just flat voltage.
  155. %% Sets parameters (e.g., stimulus durations, inter-trial intervals, etc.).
  156. S.GUI.TrialLength_seconds = 6; % In seconds
  157. S.GUI.RewardAmount_mL = 5; % In microliters
  158. S.GUI.WaterValveTime = GetValveTimes(S.GUI.RewardAmount_mL,1);
  159. S.GUI.PreStimPeriod = PoleMovement;
  160. S.GUI.RespPeriod = 1.5;
  161. S.GUI.PunishmentPeriod = 9;
  163. S.GUIPanels.Durations_and_RewardAmount = {...
  164. 'TrialLength_seconds',...
  165. 'WaterValveTime',...
  166. 'PreStimPeriod',...
  167. 'RespPeriod',...
  168. 'PunishmentPeriod',...
  169. 'RewardAmount_mL',...
  170. };
  172. BpodParameterGUI('init',S);clear ans
  176. %% Generates the sequence of trials upon user input.
  177. switch prompt
  178. case 'm'
  179. TrialSeq = [ones(1,50)+1 nan(1,995)];
  180. % TrialTypes = 1:6;
  181. % for i = 6:length(TrialSeq)
  182. % PickTrialType = randi(length(TrialTypes));
  183. % TrialSeq(i) = TrialTypes(PickTrialType);
  184. % end
  185. case 't'
  186. MaxSameTrial = 3; % Limits trial type repetition to three consecutive.
  187. TrialSeq = [zeros(1,5) nan(1,995)]; % Pre-defines five initial trials.
  188. for i = 6:length(TrialSeq)
  189. if i > MaxSameTrial
  190. if sum(TrialSeq((i-MaxSameTrial):(i-1))) == ...
  191. 0
  192. TrialSeq(i) = 1;
  193. elseif sum(TrialSeq((i-MaxSameTrial):(i-1))) == ...
  194. MaxSameTrial
  195. TrialSeq(i) = 0;
  196. else
  197. TrialSeq(i) = round(rand(1));
  198. end
  199. else
  200. TrialSeq(i) = round(rand(1));
  201. end
  202. end
  203. TrialSeq = TrialSeq+1;
  204. end
  206. BpodSystem.Data.TrialSeq = TrialSeq;
  207. BpodSystem.Data.SessionData = nan(size(TrialSeq));
  211. %% Configures the outcome plot to display the behavioral performance
  212. % during the session.
  213. BpodSystem.ProtocolFigures.GoNogoPerfOutcomePlotFig = figure(...
  214. 'Position',[200 100 1400 200],...
  215. 'name','Outcome plot',...
  216. 'numbertitle','off',...
  217. 'MenuBar', 'none',...
  218. 'Resize', 'off',...
  219. 'Color', [1 1 1]);
  221. BpodSystem.GUIHandles.GoNogoPerfOutcomePlot = axes(...
  222. 'Position',[.2 .2 .75 .7]);
  224. uicontrol(....
  225. 'Style','text',...
  226. 'String','nDisplay',...
  227. 'Position',[10 170 45 15],...
  228. 'HorizontalAlignment','left',...
  229. 'BackgroundColor', [1 1 1]);
  231. BpodSystem.GUIHandles.DisplayNTrials = uicontrol(...
  232. 'Style','edit',...
  233. 'string','90',...
  234. 'Position',[55 170 40 15],...
  235. 'HorizontalAlignment','left',...
  236. 'BackgroundColor',[1 1 1]);
  238. uicontrol(...
  239. 'Style','text',...
  240. 'String','Correct % (all): ',...
  241. 'Position',[10 140 80 15],...
  242. 'HorizontalAlignment','left',...
  243. 'BackgroundColor',[1 1 1]);
  245. BpodSystem.GUIHandles.hitpct = uicontrol(...
  246. 'Style','text',...
  247. 'string','0',...
  248. 'Position',[95 140 40 15],...
  249. 'HorizontalAlignment','left',...
  250. 'BackgroundColor',[1 1 1]);
  252. uicontrol(...
  253. 'Style','text',...
  254. 'String','Correct % (40): ',...
  255. 'Position',[10 120 80 15],...
  256. 'HorizontalAlignment','left',...
  257. 'BackgroundColor',[1 1 1]);
  259. BpodSystem.GUIHandles.hitpctrecent = uicontrol(...
  260. 'Style','text',...
  261. 'string','0',...
  262. 'Position',[95 120 40 15],...
  263. 'HorizontalAlignment','left',...
  264. 'BackgroundColor',[1 1 1]);
  266. uicontrol(...
  267. 'Style','text',...
  268. 'String','Corr rej %: ',...
  269. 'Position',[10 90 80 15],...
  270. 'HorizontalAlignment','left',...
  271. 'BackgroundColor',[1 1 1]);
  273. BpodSystem.GUIHandles.hitpctnogo = uicontrol(...
  274. 'Style','text',...
  275. 'string','0',...
  276. 'Position',[95 90 40 15],...
  277. 'HorizontalAlignment','left',...
  278. 'BackgroundColor',[1 1 1]);
  280. uicontrol(...
  281. 'Style','text',...
  282. 'String','Hit % : ',...
  283. 'Position',[10 70 80 15],...
  284. 'HorizontalAlignment','left',...
  285. 'BackgroundColor',[1 1 1]);
  287. BpodSystem.GUIHandles.hitpctgo = uicontrol(...
  288. 'Style','text',...
  289. 'string','0',...
  290. 'Position',[95 70 40 15],...
  291. 'HorizontalAlignment','left',...
  292. 'BackgroundColor',[1 1 1]);
  294. uicontrol(...
  295. 'Style','text',...
  296. 'String','Trials: ',...
  297. 'Position',[10 40 80 15],...
  298. 'HorizontalAlignment','left',...
  299. 'BackgroundColor', [1 1 1]);
  301. BpodSystem.GUIHandles.numtrials = uicontrol(...
  302. 'Style','text',...
  303. 'string','0',...
  304. 'Position',[95 40 40 15],...
  305. 'HorizontalAlignment','left',...
  306. 'BackgroundColor',[1 1 1]);
  308. uicontrol(...
  309. 'Style','text',...
  310. 'String','Rewards: ',...
  311. 'Position',[10 20 80 15],...
  312. 'HorizontalAlignment','left',...
  313. 'BackgroundColor',[1 1 1]);
  315. BpodSystem.GUIHandles.numrewards = uicontrol(...
  316. 'Style','text',...
  317. 'string','0',...
  318. 'Position',[95 20 40 15],...
  319. 'HorizontalAlignment','left',...
  320. 'BackgroundColor', [1 1 1]);
  322. BpodSystem.GUIHandles.CxnDisplay = uicontrol(...
  323. 'Style','text',...
  324. 'string','Playing',...
  325. 'Position',[130 90 70 20],...
  326. 'HorizontalAlignment','left',...
  327. 'BackgroundColor', [1 1 1]);
  329. TrialTypeOutcomePlot(BpodSystem.GUIHandles.GoNogoPerfOutcomePlot,...
  330. 'init',BpodSystem.Data.TrialSeq);
  334. %% Main loop
  335. Outcomes = nan(size(TrialSeq));StimType = cell(1);Action = cell(1);
  336. for i = 1:length(TrialSeq)
  338. % Synchronizes with BpodParameterGUI plugin and outcome plot.
  339. S = BpodParameterGUI('sync',S);
  340. TrialTypeOutcomePlot(...
  341. BpodSystem.GUIHandles.GoNogoPerfOutcomePlot,'update',...
  342. i,BpodSystem.Data.TrialSeq,Outcomes);
  344. % Displays trials on the command window and saves trials settings. For
  345. % multiple pole locations, trials are identified from 1 to 6. For two
  346. % pole locations, trials are identified as either 1 or 2. This latter
  347. % avoids an empty space between 1 and 6 on the outcome plot Y axis.
  348. switch prompt
  349. case 'm'
  350. if TrialSeq(i) == 1
  351. str = 'Go (Location PP)';
  352. StimType{i} = 'Location_PP';
  353. Action{i} = 'Hit_PP'; % Posterior defined as "go"
  354. % based on O'Connor et al.
  355. % (2010) - J Neurosci 30.
  356. elseif TrialSeq(i) == 2
  357. str = 'Go (Location P)';
  358. StimType{i} = 'Location_P';
  359. Action{i} = 'Hit_P';
  360. elseif TrialSeq(i) == 3
  361. str = 'Go (Halfway)';
  362. StimType{i} = 'Go_Halfway';
  363. Action{i} = 'Hit_H';
  364. elseif TrialSeq(i) == 4
  365. str = 'Nogo (Halfway)';
  366. StimType{i} = 'Nogo_Halfway';
  367. Action{i} = 'FalseAlarm_H';
  368. elseif TrialSeq(i) == 5
  369. str = 'Nogo (Location A)';
  370. StimType{i} = 'Location_A';
  371. Action{i} = 'FalseAlarm_A';
  372. elseif TrialSeq(i) == 6
  373. str = 'Nogo (Location AA)';
  374. StimType{i} = 'Location_AA';
  375. Action{i} = 'FalseAlarm_AA';
  376. end
  377. case 't'
  378. if TrialSeq(i) == 1
  379. str = 'Go (Location PP)';
  380. StimType{i} = 'Location_PP';
  381. Action{i} = 'Hit_PP';
  382. elseif TrialSeq(i) == 2
  383. str = 'Nogo (Location AA)';
  384. StimType{i} = 'Location_AA';
  385. Action{i} = 'FalseAlarm_AA';
  386. end
  387. end
  389. disp(['Trial #' num2str(i) ...
  390. ', ' str ' (' num2str(TrialSeq(i)) ')'])
  391. BpodSystem.Data.TrialSettings(i) = S;
  393. % Sets Bpod state matrix using the locations ("StimType{i}"), and
  394. % hits or false alarms ("Action{i}") defined above.
  395. sma = NewStateMatrix();
  396. sma = PoleLocation_Matrix(sma,S,Action{i},StimType{i});
  397. SendStateMachine(sma);
  398. RawEvents = RunStateMachine;
  400. % Allocates trials as states into the "BpodSystem.Data" structure.
  401. if ~isempty(fieldnames(RawEvents))
  402. BpodSystem.Data = AddTrialEvents(BpodSystem.Data,RawEvents);
  403. switch prompt
  404. case 'm'
  405. if TrialSeq(i) == 1 % Go trial PP
  406. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  407. States.Hit_PP(1))
  408. Outcomes(i) = 1; % Hit
  409. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  410. States.TimeOut(1))
  411. Outcomes(i) = -1; % Miss
  412. else
  413. end
  414. elseif TrialSeq(i) == 2 % Go trial P
  415. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  416. States.Hit_P(1))
  417. Outcomes(i) = 1; % Hit
  418. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  419. States.TimeOut(1))
  420. Outcomes(i) = -1; % Miss
  421. else
  422. end
  423. elseif TrialSeq(i) == 3 % Go trial halfway
  424. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  425. States.Hit_H(1))
  426. Outcomes(i) = 1; % Hit
  427. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  428. States.TimeOut(1))
  429. Outcomes(i) = -1; % Miss
  430. else
  431. end
  432. elseif TrialSeq(i) == 4 % Nogo trial halfway
  433. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}.States. ...
  434. FalseAlarm_H(1))
  435. Outcomes(i) = 0; % False alarm
  436. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ....
  437. States.TimeOut(1))
  438. Outcomes(i) = 2; % Correct rejection
  439. else
  440. end
  441. elseif TrialSeq(i) == 5 % Nogo trial A
  442. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  443. States.FalseAlarm_A(1))
  444. Outcomes(i) = 0; % False alarm
  445. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  446. States.TimeOut(1))
  447. Outcomes(i) = 2; % Correct rejection
  448. else
  449. end
  450. elseif TrialSeq(i) == 6 % Nogo trial AA
  451. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  452. States.FalseAlarm_AA(1))
  453. Outcomes(i) = 0; % False alarm
  454. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  455. States.TimeOut(1))
  456. Outcomes(i) = 2; % Correct rejection
  457. else
  458. end
  459. end
  460. case 't'
  461. if TrialSeq(i) == 1 % Go trial PP
  462. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  463. States.Hit_PP(1))
  464. Outcomes(i) = 1; % Hit
  465. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  466. States.TimeOut(1))
  467. Outcomes(i) = -1; % Miss
  468. else
  469. end
  470. elseif TrialSeq(i) == 2 % Nogo trial AA
  471. if ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  472. States.FalseAlarm_AA(1))
  473. Outcomes(i) = 0; % False alarm
  474. elseif ~isnan(BpodSystem.Data.RawEvents.Trial{i}. ...
  475. States.TimeOut(1))
  476. Outcomes(i) = 2; % Correct rejection
  477. else
  478. end
  479. end
  480. end
  481. BpodSystem.Data.SessionData(i) = Outcomes(i);
  482. BpodSystem.ProtocolSettings = S;
  484. % Saves trial outcomes and timestamps.
  485. SessionData = BpodSystem.Data;
  486. save(fullfile(basepath,'SessionData.mat'),'SessionData','-v6');
  487. end
  489. if BpodSystem.Status.BeingUsed == 0;return;end
  490. correct = Outcomes == 1 | Outcomes == 2;
  491. hitgo = Outcomes == 1;
  492. hitnogo = Outcomes == 2;
  493. correctpct = 100.*sum(correct)./i;
  494. hitpctgo = 100.*sum(hitgo)./sum(TrialSeq(1:i));
  495. hitpctnogo = 100.*sum(hitnogo)./sum(TrialSeq(1:i) == 0);
  496. inds40 = max(1,i-40+1):i;
  497. correctpctrecent = 100.*sum(correct(inds40))./numel(inds40);
  499. set(BpodSystem.GUIHandles.hitpct,...
  500. 'String', num2str(correctpct));
  502. set(BpodSystem.GUIHandles.hitpctrecent,...
  503. 'String', num2str(correctpctrecent));
  505. set(BpodSystem.GUIHandles.hitpctgo,...
  506. 'String', num2str(hitpctgo));
  508. set(BpodSystem.GUIHandles.hitpctnogo,...
  509. 'String', num2str(hitpctnogo));
  511. set(BpodSystem.GUIHandles.numtrials,...
  512. 'String',num2str(i));
  514. set(BpodSystem.GUIHandles.numrewards,...
  515. 'String', num2str(sum(hitgo)));
  517. if BpodSystem.Status.BeingUsed == 0
  518. return
  519. end
  520. end
  524. %% Bpod conversation via state matrix
  525. function sma = PoleLocation_Matrix(sma,S,CurrentAction,CurrentStimType)
  527. % Communicates the State Machine with the Analog Output Module.
  528. LoadSerialMessages('WavePlayer1',{['P' 0],['P' 1],['P' 2],['P' 3],['P' 4]});
  530. % Sets states and their timers, subsequent events, and output actions.
  531. sma = SetGlobalTimer(sma,...
  532. 'TimerID',1,...
  533. 'Duration',S.GUI.TrialLength_seconds);
  535. sma = AddState(sma,'Name','TrialStart',...
  536. 'Timer',0.1,...
  537. 'StateChangeConditions',{'Tup',CurrentStimType},...
  538. 'OutputActions',{'GlobalTimerTrig',1,'Wire1',1}); % Digital event out
  539. % of wire #1 to Intan
  541. sma = AddState(sma,'Name','Location_PP',...
  542. 'Timer',S.GUI.PreStimPeriod,...
  543. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  544. 'OutputActions',{'WavePlayer1',1}); % BNC #1 of the Analog Output
  545. % Module controls X-MCB2, and sends
  546. % a copy of the event to Intan.
  548. sma = AddState(sma,'Name','Location_P',...
  549. 'Timer', S.GUI.PreStimPeriod,...
  550. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  551. 'OutputActions',{'WavePlayer1',2}); % As above; BNC #2
  553. sma = AddState(sma,'Name','Location_A',...
  554. 'Timer', S.GUI.PreStimPeriod,...
  555. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  556. 'OutputActions',{'WavePlayer1',3}); % As above; BNC #3
  558. sma = AddState(sma,'Name','Location_AA',...
  559. 'Timer', S.GUI.PreStimPeriod,...
  560. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  561. 'OutputActions',{'WavePlayer1',4}); % As above; BNC #4
  563. sma = AddState(sma,'Name','Nogo_Halfway',...
  564. 'Timer', S.GUI.PreStimPeriod,...
  565. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  566. 'OutputActions',{'WavePlayer1',5}); % As above; BNC #5
  568. sma = AddState(sma,'Name','Go_Halfway',...
  569. 'Timer', S.GUI.PreStimPeriod,...
  570. 'StateChangeConditions',{'Tup','ResponseWindow'},...
  571. 'OutputActions',{'WavePlayer1',5}); % As above; also BNC #5
  573. sma = AddState(sma,'Name','ResponseWindow',...
  574. 'Timer',S.GUI.RespPeriod,...
  575. 'StateChangeConditions',{'Port1In',CurrentAction,'Tup','TimeOut'},...
  576. 'OutputActions',{'Wire1',1,'BNC2',1}); % Response window out of wire #1
  577. % (to Intan) and BNC #2 (for
  578. % triggering the whisker camera)
  580. sma = AddState(sma,'Name','Hit_PP',...
  581. 'Timer',S.GUI.WaterValveTime,...
  582. 'StateChangeConditions',{'Tup','TimeOut'},...
  583. 'OutputActions',{'ValveState',1,'BNC1',1}); % Rewarded response, and
  584. % corresponding event out
  585. % of the State Machine
  586. % (BNC #1) to Intan
  588. sma = AddState(sma,'Name','Hit_P',...
  589. 'Timer',S.GUI.WaterValveTime,...
  590. 'StateChangeConditions',{'Tup','TimeOut'},...
  591. 'OutputActions',{'ValveState',1,'BNC1',1}); % As above
  593. sma = AddState(sma,'Name','Hit_H',...
  594. 'Timer',S.GUI.WaterValveTime,...
  595. 'StateChangeConditions',{'Tup','TimeOut'},...
  596. 'OutputActions',{'ValveState',1,'BNC1',1}); % As above
  598. sma = AddState(sma,'Name','FalseAlarm_H',...
  599. 'Timer',0.1,...
  600. 'StateChangeConditions',{'Tup','FalseAlarm_H_timeout'},...
  601. 'OutputActions',{'BNC1',1,'PWM1',200}); % Onset of punishment time out
  602. % from BNC #1 of State Machine
  603. % to Intan, along with a
  604. % 0.1 s light cue
  606. sma = AddState(sma,'Name','FalseAlarm_H_timeout',...
  607. 'Timer',S.GUI.PunishmentPeriod-0.1,...
  608. 'StateChangeConditions',{'Tup','exit','Port1In','FalseAlarm_H_timeout'},...
  609. 'OutputActions',{'BNC1',1}); % Remaining time out period
  611. sma = AddState(sma,'Name','FalseAlarm_A',...
  612. 'Timer',0.1,...
  613. 'StateChangeConditions',{'Tup','FalseAlarm_A_timeout'},...
  614. 'OutputActions',{'BNC1',1,'PWM1',200}); % As above
  616. sma = AddState(sma,'Name','FalseAlarm_A_timeout',...
  617. 'Timer',S.GUI.PunishmentPeriod-0.1,...
  618. 'StateChangeConditions',{'Tup','exit','Port1In','FalseAlarm_A_timeout'},...
  619. 'OutputActions',{'BNC1',1}); % As above
  621. sma = AddState(sma,'Name','FalseAlarm_AA',...
  622. 'Timer',0.1,...
  623. 'StateChangeConditions',{'Tup','FalseAlarm_AA_timeout'},...
  624. 'OutputActions',{'BNC1',1,'PWM1',200}); % As above
  626. sma = AddState(sma,'Name','FalseAlarm_AA_timeout',...
  627. 'Timer',S.GUI.PunishmentPeriod-0.1,...
  628. 'StateChangeConditions',{'Tup','exit','Port1In','FalseAlarm_AA_timeout'},...
  629. 'OutputActions',{'BNC1',1}); % As above
  631. sma = AddState(sma,'Name','TimeOut',...
  632. 'Timer',10,...
  633. 'StateChangeConditions',{'GlobalTimer1_End','exit'},...
  634. 'OutputActions',{});
  635. end
  639. end

PoleLocation.m at commit 6323dd1, no license · at the source

Overview

Authors: Lezio S Bueno-Junior1, Anjesh Ghimire1, Mingxin Ding2, Brendon O Watson1
ORCID iDs: Lezio S Bueno-Junior, Anjesh Ghimire, Mingxin Ding, Brendon O Watson
  1. Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan 48109
  2. Department of Psychology, Brandeis University, Waltham, Massachusetts 02453
Institutions: University of Michigan (United States); Michigan Medicine (United States); Brandeis University (United States)
Journal: eNeuro, volume 13, issue 5, pages ENEURO.0417-25.2026
Dates: received 25 September 2025; accepted 27 April 2026; published online 26 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0417-25.2026 · PMID 42140702 · PMCID PMC13220975 · OpenAlex W7161249088
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism)
Methods: Connectivity, Spectral & time-frequency, Statistics, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: eye movements, laminar neocortical activity, motivation fluctuations, performance variations, wheel running, whisker stimulation
MeSH: Behavior, Animal*, Learning*, Motivation*, Motor Activity*, Psychomotor Performance*, Somatosensory Cortex*, Touch Perception*, Animals, Male, Mice, Mice, Inbred C57BL, Vibrissae (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Pritzker Neuropsychiatric Disorders Research Consortium; University of Michigan Neuroscience Scholars
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Animal learning can be analyzed on two timescales: task acquisition across training sessions and motivation fluctuations within training sessions. How do variations in motor and neurophysiologic activity relate to task performance over these timescales? Here, this question was examined in head-fixed mice performing a whisker-based sensory discrimination task. Male mice were trained for 12–14 daily sessions on a go/no-go task, each lasting ∼1 h to capture spontaneous performance fluctuations over minutes. Simultaneous to task performance, “nonperformance variables” were tracked, including wheel running, pupil size, eyelid aperture, and sensory cortical activity. First, motivation states were defined based on performance tendencies over minutes, leading to three state categories: persistent, disengaged, or attentive. Nonperformance variables were found to predict these states independent of task correctness. Then, when further parsing these states by the go/no-go outcomes of hit, miss, false alarm, or correct rejection, learning-like changes were detected in wheel running, eye movements, and brain activity. Thus, learning over days and motivation fluctuations over minutes form a continuum, as evidenced by changes in motor and physiologic activity variables not directly controlled by task contingencies, even during periods of suboptimal performance in well-trained subjects. These findings improve the understanding of performance variations and implicit learning, in addition to contributing a framework for the analysis of task performance indirectly from motor and neurophysiologic activity.

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 16 matches between paragraphs and lines of code.

brendonw1/watsonlabcode

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6323dd12e4266536b899a2676fcf92aa49d605b6, 9 September 2026
Languages: MATLAB (1957), C (73), C++ (2), Shell (2), Python (1)
Size: 2,630 files, 2,035 scripts
Software Heritage: not archived
Found in: “Code accessibility”
Holds: README, tests, documentation, 6 notebooks
Not found: license file, CITATION.cff, environment file, continuous integration
Tools: Statistics and Machine Learning Toolbox (156 files), Signal Processing Toolbox (40 files), Image Processing Toolbox (17 files), boundedline (11 files), Chronux (5 files), Optimization Toolbox (5 files), CircStat (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2,000 files
At the source: github.com/brendonw1/WatsonLabCode/tree/master/LearningMotivationStudy

Code accessibility

The code for classification of motivation states was developed interactively using the Classification Learner App in MATLAB and is freely available at https://github.com/brendonw1/WatsonLabCode/tree/master/LearningMotivationStudy.

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;
  • 1,999 scripts, each with its path and the digest of its content;
  • 16 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

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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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 12 MeSH terms, 2 funders, 60 references.

Cite

This paper

Bueno-Junior, L. S., Ghimire, A., Ding, M., & Watson, B. O. (2026). Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice. eNeuro, 13(5), ENEURO.0417-25.2026. https://doi.org/10.1523/eneuro.0417-25.2026

BibTeX

@article{buenojunior2026learning,
author = {Bueno-Junior, Lezio S and Ghimire, Anjesh and Ding, Mingxin and Watson, Brendon O},
title = {{Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice}},
journal = {eNeuro},
year = {2026},
month = may,
volume = {13},
number = {5},
pages = {ENEURO.0417--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0417-25.2026},
url = {https://doi.org/10.1523/eneuro.0417-25.2026},
pmid = {42140702},
pmcid = {PMC13220975}
}

RIS

TY - JOUR
AU - Bueno-Junior, Lezio S
AU - Ghimire, Anjesh
AU - Ding, Mingxin
AU - Watson, Brendon O
TI - Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/05/27
VL - 13
IS - 5
SP - ENEURO.0417
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0417-25.2026
UR - https://doi.org/10.1523/eneuro.0417-25.2026
LA - en
ER -

CSL-JSON

{
"id": "10.1523/eneuro.0417-25.2026",
"type": "article-journal",
"title": "Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice",
"container-title": "eNeuro",
"author": [
{
"family": "Bueno-Junior",
"given": "Lezio S"
},
{
"family": "Ghimire",
"given": "Anjesh"
},
{
"family": "Ding",
"given": "Mingxin"
},
{
"family": "Watson",
"given": "Brendon O"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "5",
"page": "ENEURO.0417-25.2026",
"DOI": "10.1523/eneuro.0417-25.2026",
"PMID": "42140702",
"PMCID": "PMC13220975",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0417-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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