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Behavioral engagement facilitates auditory neuron responses beyond their receptive fields.

Code ↔ Paper

8 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.

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  1. [1] § Methods › Statistics, mixed-effects model, and behavioral sensitivity ↔ Fig4_Control_Miss_Dprime/Fig4d_Dprime_vs_MI_Target.m, lines 60–172 · score 0.73 · median FA rate, median hit rate, behavioral sensitivity, MI, modulation, model
  2. [2] § Methods › Statistics, mixed-effects model, and behavioral sensitivity ↔ Fig4_Control_Miss_Dprime/Fig4d_Dprime_illustrate_real.m, the whole file · a weak match · score 0.62 · noise distribution, FA rate, signal, curve, median, Figure 4
  3. [3] § Methods › Spatial discrimination task ↔ Fig1_oneDemo/M9X0831.m, lines 181–240 · score 0.61 · background alternations, air, sham, puff, reward, lick
  4. [4] § Methods › Spatial discrimination task ↔ Fig2_twoDemo/M71V2523.m, lines 181–240 · score 0.60 · background alternations, air, sham, puff, reward, lick
  5. [5] § Results › Attentional modulation is context-independent and behaviorally relevant ↔ Fig4_Control_Miss_Dprime/Fig4d_Dprime_vs_MI_Target.m, lines 60–172 · score 0.60 · alarm rate, behavioral sensitivity, preparation, MI, median, modulation
  6. [6] § Methods › Statistics, mixed-effects model, and behavioral sensitivity ↔ Fig4_Control_Miss_Dprime/Fig4d_Dprime_illustrate_real.m, the whole file · a weak match · score 0.59 · median hit rate, FA rate, MI, Figure 4
  7. [7] § Methods › Acoustic stimuli and receptive field characterization ↔ Fig1_oneDemo/M9X0831.m, lines 181–240 · score 0.53 · eye position, FT28D, delivered, speakers, stimuli
  8. [8] § Methods › Acoustic stimuli and receptive field characterization ↔ Fig2_twoDemo/M71V2523.m, lines 181–240 · score 0.53 · eye position, FT28D, delivered, speakers, stimuli

Paper

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The authors' code

MATLAB · 201 lines · 8 KB · no license · 2 matches

  1. % by CCG @ 2025-12-23
  3. clear; clc; close all
  4. load('SRF_shift_test_analysis.mat')
  5. rP_trial_sig = [] ;
  6. rP_trial_nonsig = [] ;
  7. rA_trial_sig = [] ;
  8. rA_trial_nonsig = [] ;
  9. AvP_units_raw = SRF_shift_test_analysis.targets_vs_passive; %% 1353*37 double, include both Hits&Misses
  10. N_trials = size(AvP_units_raw, 1);
  11. id_rate_P = 1 ;
  12. id_rate_A = 2 ;
  13. id_sig = 19 ;
  14. id_sig_In = 20 ;
  15. id_sig_De = 21 ;
  16. MI_all = [];
  17. MI_In = [];
  18. MI_De = [];
  19. id_hit_rate = 25 ;
  20. id_mean_hit_rate = 26 ;
  21. id_mean_false_alarm_rate = 27 ;
  22. Dprime_all = [];
  23. Hit_all = [];
  24. FA_all = [];
  25. for n = 1 : N_trials
  26. p_temp = AvP_units_raw(n, id_rate_P);
  27. a_temp = AvP_units_raw(n, id_rate_A);
  28. hit_rate = AvP_units_raw(n, id_hit_rate);
  29. hit_rate(hit_rate>0.99) = 0.99; hit_rate(hit_rate<0.01) = 0.01;
  30. FA_rate = AvP_units_raw(n, id_mean_false_alarm_rate);
  31. FA_rate(FA_rate>0.99) = 0.99; FA_rate(FA_rate<0.01) = 0.01;
  32. if AvP_units_raw(n, id_sig)==1 && ~isnan(hit_rate+FA_rate) %>1 spike and significant different from spontaneous && existence of hit and false alarm
  33. rP_trial_sig = [rP_trial_sig; p_temp];
  34. rA_trial_sig = [rA_trial_sig; a_temp];
  35. MI_temp = (a_temp - p_temp)/(a_temp + p_temp) ;
  36. if abs(MI_temp)>1
  37. MI_temp = sign(MI_temp);
  38. end
  39. MI_all = [MI_all; MI_temp] ; %=SRF_shift_test_analysis.MI_hits_vs_passive_sig
  40. if AvP_units_raw(n, id_sig_In)==1
  41. MI_In = [MI_In; MI_temp] ; %=SRF_shift_test_analysis.MI_hits_vs_passive_sig_increases
  42. elseif AvP_units_raw(n, id_sig_De)==1
  43. MI_De = [MI_De; MI_temp] ;
  44. end
  46. Hit_all = [Hit_all; hit_rate];
  47. FA_all = [FA_all; FA_rate];
  48. Dprime_temp_hit = norminv(hit_rate);
  49. Dprime_temp_FA = norminv(FA_rate);
  50. Dprime_temp = Dprime_temp_hit - Dprime_temp_FA;
  51. if isnan(Dprime_temp)
  52. disp(n)
  53. end
  54. Dprime_all = [Dprime_all; Dprime_temp] ;
  55. elseif AvP_units_raw(n, id_sig)==0
  56. rP_trial_nonsig = [rP_trial_nonsig; p_temp];
  57. rA_trial_nonsig = [rA_trial_nonsig; a_temp];
  58. end
  59. end
  60. %%
  61. pos=get(0,'ScreenSize'); X_size=pos(3);Y_size=pos(4);
  62. figure('position',[X_size*0.1 Y_size*0.3 X_size*0.45 Y_size*0.45]);
  63. sz=30;
  65. hit_pos = Hit_all(MI_all > 0);
  66. FA_pos = FA_all(MI_all > 0);
  67. dprime_pos = Dprime_all(MI_all > 0);
  68. mi_pos = MI_all(MI_all > 0);
  70. hit_neg = Hit_all(MI_all < 0);
  71. FA_neg = FA_all(MI_all < 0);
  72. dprime_neg = Dprime_all(MI_all < 0);
  73. mi_neg = MI_all(MI_all < 0);
  75. med_d_pos = median(dprime_pos);
  76. med_d_neg = median(dprime_neg);
  77. med_mi_pos = median(mi_pos);
  78. med_mi_neg = median(mi_neg);
  80. fprintf('--- Statistics ---\n');
  81. fprintf('Condition MI > 0 (Purple): Median Hit = %.3f, Median FA = %.3f\n', median(hit_pos), median(FA_pos));
  82. fprintf('Condition MI > 0 (Purple): Median d'' = %.3f, Median MI = %.3f\n', med_d_pos, med_mi_pos);
  83. fprintf('Condition MI > 0 (Purple): num. d'' >=1 # %.0f, percent = %.2f\n', numel(find(dprime_pos>=1)), numel(find(dprime_pos>=1))/numel(dprime_pos));
  84. fprintf('Condition MI > 0 (Purple): num. d'' >=1.5 # %.0f, percent = %.2f\n', numel(find(dprime_pos>=1.5)), numel(find(dprime_pos>=1.5))/numel(dprime_pos));
  86. fprintf('Condition MI < 0 (Green) : Median Hit = %.3f, Median FA = %.3f\n', median(hit_neg), median(FA_neg));
  87. fprintf('Condition MI < 0 (Green) : Median d'' = %.3f, Median MI = %.3f\n', med_d_neg, med_mi_neg);
  88. fprintf('Condition MI < 0 (Green): num. d'' >=1 # %.0f, percent = %.2f\n', numel(find(dprime_neg>=1)), numel(find(dprime_neg>=1))/numel(dprime_neg));
  89. fprintf('Condition MI < 0 (Green): num. d'' >=1.5 # %.0f, percent = %.2f\n', numel(find(dprime_neg>=1.5)), numel(find(dprime_neg>=1.5))/numel(dprime_neg));
  91. % scatter(dprime_pos, mi_pos, sz*1, 'x', 'MarkerEdgeColor', rgb('BlueViolet'),'LineWidth', 1); hold on
  92. % scatter(med_d_pos, med_mi_pos, sz*2, 'x', 'MarkerEdgeColor', rgb('Black'),'LineWidth', 3);
  93. % scatter(dprime_neg, mi_neg, sz*1, 'x', 'MarkerEdgeColor', rgb('Green'),'LineWidth', 1);
  94. % scatter(med_d_neg, med_mi_neg, sz*2, 'x', 'MarkerEdgeColor', rgb('Black'),'LineWidth', 3);
  95. % xlim([-3 5]);
  96. % xlabel('Behavioral sensitivity (d'')')
  97. % ylabel('Modulation index (MI)')
  99. scatter(mi_pos, dprime_pos, sz*1, 'x', 'MarkerEdgeColor', rgb('BlueViolet'),'LineWidth', 1); hold on
  100. scatter(med_mi_pos, med_d_pos, sz*2, 'x', 'MarkerEdgeColor', rgb('Black'),'LineWidth', 3);
  101. scatter(mi_neg, dprime_neg, sz*1, 'x', 'MarkerEdgeColor', rgb('Green'),'LineWidth', 1);
  102. scatter(med_mi_neg, med_d_neg, sz*2, 'x', 'MarkerEdgeColor', rgb('Black'),'LineWidth', 3);
  103. % ylim([-3 5]); % full range
  104. ylim([-1 4]);
  105. yticks(-1:1:4)
  106. ylabel('Behavioral sensitivity (d'')')
  107. xlabel('Modulation index (MI)')
  109. pbaspect([1 1 1])
  110. % %% Extract variables for clearer code
  111. % % --- 1. Data Preparation ---
  112. % % fa_rate = AvP_units_raw(AvP_units_raw(:, id_sig)==1, id_mean_false_alarm_rate);
  113. % % hit_rate = AvP_units_raw(AvP_units_raw(:, id_sig)==1, id_hit_rate);
  114. % % firing_rate = AvP_units_raw(AvP_units_raw(:, id_sig)==1, id_rate_A);
  115. % fa_rate = AvP_units_raw(:, id_mean_false_alarm_rate);
  116. % hit_rate = AvP_units_raw(:, id_hit_rate);
  117. % firing_rate = AvP_units_raw(:, id_rate_P); %>>>>>>>>>>>>>>Either passive or active*****************
  118. %
  119. % % --- 2. Statistical Analysis (Linear Regression) ---
  120. % % We use fitlm to get R-squared and P-values easily
  121. % mdl_fa = fitlm(fa_rate, firing_rate);
  122. % mdl_hit = fitlm(hit_rate, firing_rate);
  123. %
  124. % % Extract Stats
  125. % r2_fa = mdl_fa.Rsquared.Ordinary;
  126. % p_fa = mdl_fa.Coefficients.pValue(2); % p-value of the slope
  127. %
  128. % r2_hit = mdl_hit.Rsquared.Ordinary;
  129. % p_hit = mdl_hit.Coefficients.pValue(2); % p-value of the slope
  130. %
  131. % % --- 3. Visualization ---
  132. % figure; hold on;
  133. %
  134. % % A. Plot Scatter Points
  135. % % Note: 0 values in firing_rate will not show on Log scale.
  136. % % You might need to add a small constant (e.g., +0.1) if you have 0 Hz units.
  137. % h1 = scatter(fa_rate, firing_rate, 30, rgb('BlueViolet'), 'filled', 'MarkerFaceAlpha', 0.3);
  138. % h2 = scatter(hit_rate, firing_rate, 30, rgb('Green'), 'filled', 'MarkerFaceAlpha', 0.3);
  139. %
  140. % % B. Plot Linear Fit Lines
  141. % % We define a smooth range for plotting the lines
  142. % x_grid = linspace(0, 1, 100)';
  143. %
  144. % % Predict Y values based on the linear models
  145. % y_pred_fa = predict(mdl_fa, x_grid);
  146. % y_pred_hit = predict(mdl_hit, x_grid);
  147. %
  148. % % Plot the lines
  149. % plot(x_grid, y_pred_fa, 'Color',rgb('BlueViolet'), 'LineWidth', 2);
  150. % plot(x_grid, y_pred_hit, 'Color',rgb('Green'), 'LineWidth', 2);
  151. %
  152. % % --- 4. Formatting Axes & Labels ---
  153. % % set(gca, 'YScale', 'log'); % Set Y-axis to Log10 scale
  154. %
  155. % % Custom Y-Ticks as requested (-20:20:100)
  156. % % Note: Log axis will only show the positive ticks (20, 40, 60, 80, 100)
  157. % yticks(-20:20:100);
  158. % ylim([-20 100]); % Adjust lower limit to >0 so log plot works (e.g., 1 or 0.1)
  159. %
  160. % xlabel('Behavioral Rate (Prob.)');
  161. % ylabel('Firing Rate (Hz)');
  162. % title('Modulation Effect: Hit vs False Alarm');
  163. %
  164. % % --- 5. Add Legend with Stats ---
  165. % % Create dynamic legend labels with R2 and p-values
  166. % legend_str_fa = sprintf('False Alarm (R^2=%.5f, p=%.5f)', r2_fa, p_fa);
  167. % legend_str_hit = sprintf('Hit Rate (R^2=%.5f, p=%.5f)', r2_hit, p_hit);
  168. %
  169. % legend([h1, h2], {legend_str_fa, legend_str_hit}, 'Location', 'best');
  170. %
  171. % grid on;
  172. % hold off;
  173. %% Color-Coded 2D Scatter
  174. % figure; % show how False Alarm Rate influences that relationship
  175. % % X = Hit Rate, Y = Firing Rate, Color = False Alarm Rate
  176. % scatter(hit_rate, firing_rate, 50, fa_rate, 'filled', 'MarkerFaceAlpha', 0.3);
  177. %
  178. % xlabel('Hit Rate');
  179. % ylabel('Firing Rate');
  180. % title('Hit Rate vs. Firing Rate (Colored by FA Rate)');
  181. %
  182. % % Create a colormap (e.g., cool/warm) to clearly see low vs high FA
  183. % colormap('jet');
  184. % c = colorbar;
  185. % c.Label.String = 'False Alarm Rate';
  186. % grid on;
  187. %%
  188. % figure;
  189. % % X = FA, Y = Hit, Z = Firing Rate
  190. % scatter3(fa_rate, hit_rate, firing_rate, 40, firing_rate, 'filled');
  191. %
  192. % xlabel('False Alarm Rate');
  193. % ylabel('Hit Rate');
  194. % zlabel('Firing Rate');
  195. % title('Joint Effect of Hit and FA Rates on Firing');
  196. %
  197. % % Add a colorbar to emphasize the Z-axis (Firing Rate)
  198. % c = colorbar;
  199. % c.Label.String = 'Firing Rate';
  200. % view(45, 30); % Adjust angle for better visibility
  201. % grid on;

Fig4d_Dprime_vs_MI_Target.m at commit f59664e, no license · at the source

Overview

Authors: Chenggang Chen1, Evan D. Remington1, Xiaoqin Wang1
ORCID iDs: Chenggang Chen, Xiaoqin Wang
  1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Institutions: Johns Hopkins University (United States); Johns Hopkins Medicine (United States)
Journal: PLoS biology, volume 24, issue 3, article e3003707
Dates: received 10 October 2025; accepted 27 February 2026; published online 13 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003707 · PMID 41824472 · PMCID PMC13012619 · OpenAlex W7135208286
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Single-unit activity, calcium imaging
MeSH: Auditory Cortex*, Behavior, Animal*, Neurons*, Acoustic Stimulation, Action Potentials, Animals, Auditory Perception, Callithrix, Male, Models, Neurological, Sound Localization (* major topic)
Journal subjects: Biology and Life Sciences, Psychology, Behavior, Social Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Physiology, Sensory Physiology, Auditory System, Auditory Cortex, Sensory Systems, Anatomy, Brain, Medicine and Health Sciences, Neuronal Tuning, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Animal Models, Marmosets, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Monkeys, New World monkeys, Zoology, Animal Behavior, Electrophysiology, Membrane Potential, Cognitive Science, Cognitive Psychology, Attention
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (DC003180)
Citations: not cited yet (Europe PMC); 109 references in the paper

Abstract

In the auditory cortex, neural responses to stimuli inside receptive fields (RFs) can be further facilitated by behavioral demands, such as attending to a spatial location. It is less clear how off-RF stimuli modulate neural responses and contribute to behavioral tasks. Our recent study revealed a particular form of location-specific facilitation evoked by repeated stimulation from an off-RF location, suggesting behavioral modulation of spatial RFs. To further explore this question, we trained marmosets to attend to sound locations that were either inside or outside the RFs of auditory cortical neurons. The majority of neurons showed increased firing rates at target locations inside their RFs. Interestingly, this increase also occurred outside the RFs, sometimes exceeding the responses at the RF center during passive listening. This task-related off-RF facilitation was much more common in the caudal area than in the rostral area and the primary auditory cortex. A normalization model reproduced the off-RF facilitation using widespread suppression. The model’s prediction was confirmed by experimental observations of widespread reductions in firing rate and hyperpolarized membrane potentials for off-RF stimuli. These results suggest that behavioral task demands recruit a broader range of neurons than those that are responsive to a target sound in the passive state.

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

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Zenodo 18736680

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
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 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: zenodo.org/records/18736680

ccg1988/Attention_PLoS_Biology_2026

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f59664ebdb29b448042b15f91a4caa2ad7238c78, 22 February 2026
Languages: MATLAB (125)
Size: 277 files, 125 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files), Image Processing Toolbox (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
126 files
At the source: github.com/ccg1988/Attention_PLoS_Biology_2026

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

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Data

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Data Availability

All relevant data are within the paper and its Supporting information files. Raw and processed data is available at Zenodo (https://zenodo.org/records/18736680) and GitHub (https://github.com/ccg1988/Attention_PLoS_Biology_2026).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 11 MeSH terms, 1 funder, 109 references.

Cite

This paper

Chen, C., Remington, E. D., & Wang, X. (2026). Behavioral engagement facilitates auditory neuron responses beyond their receptive fields. PLoS biology, 24(3), e3003707. https://doi.org/10.1371/journal.pbio.3003707

BibTeX

@article{chen2026behavioral,
author = {Chen, Chenggang and Remington, Evan D. and Wang, Xiaoqin},
title = {{Behavioral engagement facilitates auditory neuron responses beyond their receptive fields}},
journal = {PLoS biology},
year = {2026},
month = mar,
volume = {24},
number = {3},
pages = {e3003707},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003707},
url = {https://doi.org/10.1371/journal.pbio.3003707},
pmid = {41824472},
pmcid = {PMC13012619}
}

RIS

TY - JOUR
AU - Chen, Chenggang
AU - Remington, Evan D.
AU - Wang, Xiaoqin
TI - Behavioral engagement facilitates auditory neuron responses beyond their receptive fields
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/03/13
VL - 24
IS - 3
SP - e3003707
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003707
UR - https://doi.org/10.1371/journal.pbio.3003707
LA - en
ER -

CSL-JSON

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"title": "Behavioral engagement facilitates auditory neuron responses beyond their receptive fields",
"container-title": "PLoS biology",
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"family": "Chen",
"given": "Chenggang"
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"family": "Remington",
"given": "Evan D."
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{
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"given": "Xiaoqin"
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"container-title-short": "PLoS Biol",
"volume": "24",
"issue": "3",
"page": "e3003707",
"DOI": "10.1371/journal.pbio.3003707",
"PMID": "41824472",
"PMCID": "PMC13012619",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003707",
"language": "en",
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"date-parts": [
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