Inhibitory columnar feedback neurons are involved in motion processing in Drosophila.

Code ↔ Paper

7 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 7 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials and methods › Processing of behavioral data ↔ Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt2_masterScript.m, lines 76–120 · score 0.66 · 0.75–1 s, lowest, 0.75 s, peak, edge, 0.5 s
[2] § Materials and methods › Processing of behavioral data ↔ Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt4_masterScript.m, lines 96–197 · score 0.64 · 0.75–1 s, lowest, 0.75 s, 0.5 s, peak, behavioral
[3] § Results › C2 and C3 are ON-selective neurons that sample information across few columns ↔ subscripts/Gaussian_Fit_C2C3.m, the whole file · a weak match · score 0.64 · full width half, Gaussian fit, receptive fields, FWHM
[4] § Results › C2 and C3 are ON-selective neurons that sample information across few columns ↔ subscripts/Gaussian_Fit_Mi1.m, the whole file · a weak match · score 0.64 · full width half, Gaussian fit, receptive fields, FWHM
[5] § Materials and methods › Processing of two photon data › Response quantification › Space-time receptive field mapping ↔ subscripts/Gaussian_Fit_C2C3.m, the whole file · a weak match · score 0.62 · full width half, Gaussian fit, FWHM, receptive, field
[6] § Materials and methods › Processing of two photon data › Response quantification › Space-time receptive field mapping ↔ subscripts/Gaussian_Fit_Mi1.m, the whole file · a weak match · score 0.62 · full width half, Gaussian fit, FWHM, receptive, field
[7] § Materials and methods › Processing of behavioral data ↔ Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt1_masterScript.m, lines 79–137 · score 0.52 · single edge, phase, slopes, 0.45 s, fit, 0.75 s

Paper

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The authors' code

MATLAB · 49 lines · 962 B · MIT · 2 matches

function FWHM=Gaussian_Fit_C2C3(SpEx_Az_Max,SpEx_El_Max);
%This function fits gaussians to the maximum spatial receptive field
FWHM_Az=[];
FWHM_El=[];
for i=1:size(SpEx_Az_Max,2)
% DATA
fm = SpEx_Az_Max(:,i)';
xm = [1:12]*5; %xm = xm';
% FIT GAUSSIAN
f = fit(xm.',fm.','gauss1');
% figure; plot(f,xm,fm)
% hold on
% plot(xm,fm)
% CALC FWHM = full width half maximum = sigma(c1) * 2.3548;
FWHM_Az=[FWHM_Az, f.c1*2.3548];
end
for i=1:size(SpEx_El_Max,2)
% DATA
fm = SpEx_El_Max(:,i)';
xm = [1:12]*5; %xm = xm';
% FIT GAUSSIAN
f = fit(xm.',fm.','gauss1');
% figure; plot(f,xm,fm)
% hold on
% plot(xm,fm)
% CALC FWHM = full width half maximum = sigma(c1) * 2.3548;
FWHM_El=[FWHM_El, f.c1*2.3548];
end
% PLOT
% Put Data together
FWHM=nan(2,50);
FWHM(1,1:length(FWHM_Az))=FWHM_Az;
FWHM(2,1:length(FWHM_El))=FWHM_El;
end

Gaussian_Fit_C2C3.m at commit 745c611, under MIT · at the source

Overview

Authors: Miriam Henning¹, Madhura D Ketkar¹, Teresa Lüffe¹, Daryl M Gohl^2,3, Thomas R Clandinin⁴, Marion Silies^1,5

ORCID iDs: Miriam Henning, Madhura D Ketkar, Daryl M Gohl, Thomas R Clandinin, Marion Silies

Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz Mainz Germany
University of Minnesota Genomics Center Minneapolis United States
Department of Genetics, Cell Biology, and Developmental Biology, University of Minnesota Minneapolis United States
Department of Neurobiology, Stanford University Stanford United States
Institute for Quantitative and Computational Biosciences (IQCB), Johannes Gutenberg University Mainz Mainz Germany

Institutions: Johannes Gutenberg University Mainz (Germany); University of Minnesota (United States); Stanford University (United States)

Journal: eLife, volume 14, article RP108529

Dates: published online 26 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.108529 · PMID 42187121 · PMCID PMC13211874 · OpenAlex W4415702608

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: drosophila (organism)

Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: vision, inhibitory feedback neurons, direction selectivity, neural circuits, motion vision, Drosophila, D. melanogaster

MeSH: Drosophila melanogaster*, Feedback, Physiological*, GABAergic Neurons*, Motion Perception*, Neurons*, Visual Pathways*, Animals, Neural Inhibition, Photic Stimulation (* major topic)

Journal subjects: Neuroscience

Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: European Research Council (101045003); Deutsche Forschungsgemeinschaft (CRC1080 project C06)

Citations: not cited yet (Europe PMC); 94 references in the paper

Research resources: RRID:BDSC_32229, UAS-DenMark, UAS-syt.eGFP RRID:BDSC_33065, 20xUAS-IVS- GCaMP6f attP40 RRID:BDSC_42747, UAS-shits RRID:BDSC_44222, lexAop-GCaMP6f-p10su(Hw)attp5 RRID:BDSC_44277, R19F01-lexAattp40 RRID:BDSC_52547, R59E08-LexAattP40 RRID:BDSC_52832, UAS-LexA.DBD RRID:BDSC_56528, Gad1MI09277-p65AD RRID:BDSC_60322, PBac{IT.GAL4}0081 RRID:BDSC_62703, PBac{IT.GAL4}0168 RRID:BDSC_62706, PBac{IT.GAL4}0301 RRID:BDSC_62767, PBac{IT.GAL4}0470 RRID:BDSC_63341, PBac{IT.GAL4}0564 RRID:BDSC_63411, PBac{IT.GAL4}0619 RRID:BDSC_63449, PBac{IT.GAL4}0756 RRID:BDSC_63499, PBac{IT.GAL4}0651 RRID:BDSC_63731, PBac{IT.GAL4}0787 RRID:BDSC_63782, PBac{IT.GAL4}0913 RRID:BDSC_63892, PBac{IT.GAL4}0940 RRID:BDSC_63911, PBac{IT.GAL4}0980 RRID:BDSC_63936, PBac{IT.GAL4}1037 RRID:BDSC_63975, PBac{IT.GAL4}0396 RRID:BDSC_64718, PBac{IT.GAL4}0669 RRID:BDSC_64737, UAS-KCNJ2.EGFP(Kir2.1)7 RRID:BDSC_6595, R25B02-Gal4.DBDattP2 RRID:BDSC_68969, R48D11-Gal4.DBDattP2 RRID:BDSC_69028, R20C11-p65.ADattP40 RRID:BDSC_70106, R26H02-p65.ADattP40 RRID:BDSC_70159

Abstract

Visual motion information is essential to guiding the movements of many animals. The establishment of direction-selective signals, a hallmark of motion detection, is considered a core neural computation and has been characterized extensively in primates, mice, and fruit flies. In flies, the circuits that produce direction-selective signals rely on feedforward visual pathways that connect peripheral visual inputs to the dendrites of the ON and OFF-direction-selective cells. Here, we describe a novel role for feedback inhibition in motion computation. Two GABAergic neurons, C2 and C3, connect to neurons upstream of the direction-selective T4 and T5 cells, and blocking C2 and C3 affects direction selectivity in T4/T5. In the ON pathway, this is likely achieved by C2-mediated suppression of responses in the major T4 input neuron Mi1. Together, C2 and C3 suppress responses to non-preferred stimuli in both T4 and T5. At the behavioral level, feedback inhibition temporally sharpens responses to ON-moving stimuli, enhancing the fly’s ability to discriminate visual stimuli that occur in quick succession. GABAergic inhibitory feedback neurons thus constitute an essential component within the circuitry that computes visual motion.

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 7 matches between paragraphs and lines of code.

silieslab/C2C3_feedback_Henning

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 745c6114b72a61dabc9389a1f024dbe22f679edf, 17 March 2026

Languages: MATLAB (104), Python (4)

Size: 226 files, 108 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file, 1 notebook

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: CircStat (33 files), Statistics and Machine Learning Toolbox (26 files), Violinplot-Matlab (3 files), NumPy (2 files), h5py (1 file), Optimization Toolbox (1 file), Matplotlib (1 file), SciPy (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

110 files

Figure2_SourceCode1_ConnectivityC2C3.m, MATLAB, 312 lines
Figure3_SourceCode1_Plot_ONOFF_FlashAnalysis_C2C3.m, MATLAB, 297 lines
Figure3_SourceCode2_Polarplots_C2C3.m, MATLAB, 115 lines
Figure3_SourceCode3_STRFs_C2C3.m, MATLAB, 163 lines
Figure4_SourceCode1_Plot_ONOFF_FlashAnalysis_Mi1.m, MATLAB, 109 lines
Figure4_SourceCode2_STRFs_Mi1.m, MATLAB, 82 lines
Figure5_SourceCode1_Plot_ONOFF_FlashAnalysis_T4T5.m, MATLAB, 991 lines
Figure6_SourceCode1_T4T5_DS_Responses.m, MATLAB, 133 lines
Figure7_SourceCode1_behavior analysis code/accumulatebyindex.m, MATLAB, 28 lines
Figure7_SourceCode1_behavior analysis code/assignstimstarts_groups_mk.m, MATLAB, 49 lines
Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt1_masterScript.m, MATLAB, 207 lines, 1 match
Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt2_masterScript.m, MATLAB, 168 lines, 1 match
Figure7_SourceCode1_behavior analysis code/behaviour_analysis_expt4_masterScript.m, MATLAB, 236 lines, 1 match
Figure7_SourceCode1_behavior analysis code/calculate_fly_position_mk.m, MATLAB, 39 lines
Figure7_SourceCode1_behavior analysis code/plot_err_patch.m, MATLAB, 14 lines
Figure7_SourceCode1_behavior analysis code/plot_err_patch_v2.m, MATLAB, 18 lines
Figure7_SourceCode1_behavior analysis code/plot_pdata_traces.m, MATLAB, 203 lines
Figure7_SourceCode1_behavior analysis code/plot_pdata_traces_2xEdge_500ms.m, MATLAB, 205 lines
Figure7_SourceCode1_behavior analysis code/plot_pdata_traces_2xEdge_750ms.m, MATLAB, 205 lines
Figure7_SourceCode1_behavior analysis code/process_stimuli_mk.m, MATLAB, 50 lines
Figure7_SourceCode1_behavior analysis code/read_data_files_new_mk.m, MATLAB, 127 lines
PythonCode_for_STRF_analysis/RUN_ME_FIRST.py, Python, 85 lines
PythonCode_for_STRF_analysis/master_script.py, Python, 38 lines
PythonCode_for_STRF_analysis/plot_RFwithmasks.py, Python, 127 lines
PythonCode_for_STRF_analysis/rf_tools.py, Python, 846 lines
subscripts/Average_STRFsC2C3.m, MATLAB, 261 lines
subscripts/Average_STRFsMi1.m, MATLAB, 142 lines
subscripts/CircStat2012a/Contents.m, MATLAB, 63 lines
subscripts/CircStat2012a/circ_ang2rad.m, MATLAB, 11 lines
subscripts/CircStat2012a/circ_axial.m, MATLAB, 29 lines
subscripts/CircStat2012a/circ_axialmean.m, MATLAB, 41 lines
subscripts/CircStat2012a/circ_clust.m, MATLAB, 151 lines
subscripts/CircStat2012a/circ_clust_mh.m, MATLAB, 155 lines
subscripts/CircStat2012a/circ_cmtest.m, MATLAB, 90 lines
subscripts/CircStat2012a/circ_confmean.m, MATLAB, 79 lines
subscripts/CircStat2012a/circ_corrcc.m, MATLAB, 53 lines
subscripts/CircStat2012a/circ_corrcl.m, MATLAB, 50 lines
subscripts/CircStat2012a/circ_dist.m, MATLAB, 28 lines
subscripts/CircStat2012a/circ_dist2.m, MATLAB, 36 lines
subscripts/CircStat2012a/circ_hktest.m, MATLAB, 253 lines
subscripts/CircStat2012a/circ_kappa.m, MATLAB, 57 lines
subscripts/CircStat2012a/circ_ktest.m, MATLAB, 59 lines
subscripts/CircStat2012a/circ_kuipertest.m, MATLAB, 113 lines
subscripts/CircStat2012a/circ_kurtosis.m, MATLAB, 51 lines
subscripts/CircStat2012a/circ_mean.m, MATLAB, 56 lines
subscripts/CircStat2012a/circ_median.m, MATLAB, 72 lines
subscripts/CircStat2012a/circ_medtest.m, MATLAB, 46 lines
subscripts/CircStat2012a/circ_moment.m, MATLAB, 69 lines
subscripts/CircStat2012a/circ_mtest.m, MATLAB, 69 lines
subscripts/CircStat2012a/circ_otest.m, MATLAB, 81 lines
subscripts/CircStat2012a/circ_plot.m, MATLAB, 143 lines
subscripts/CircStat2012a/circ_r.m, MATLAB, 62 lines
subscripts/CircStat2012a/circ_rad2ang.m, MATLAB, 11 lines
subscripts/CircStat2012a/circ_raotest.m, MATLAB, 130 lines
subscripts/CircStat2012a/circ_rtest.m, MATLAB, 75 lines
subscripts/CircStat2012a/circ_samplecdf.m, MATLAB, 74 lines
subscripts/CircStat2012a/circ_skewness.m, MATLAB, 52 lines
subscripts/CircStat2012a/circ_stats.m, MATLAB, 66 lines
subscripts/CircStat2012a/circ_std.m, MATLAB, 57 lines
subscripts/CircStat2012a/circ_symtest.m, MATLAB, 40 lines
subscripts/CircStat2012a/circ_var.m, MATLAB, 57 lines
subscripts/CircStat2012a/circ_vmpar.m, MATLAB, 38 lines
subscripts/CircStat2012a/circ_vmpdf.m, MATLAB, 46 lines
subscripts/CircStat2012a/circ_vmrnd.m, MATLAB, 87 lines
subscripts/CircStat2012a/circ_vtest.m, MATLAB, 77 lines
subscripts/CircStat2012a/circ_wwtest.m, MATLAB, 158 lines
subscripts/CircStat2012a/examples/example1.m, MATLAB, 195 lines
subscripts/CircStat2012a/examples/example2.m, MATLAB, 156 lines
subscripts/CircStat2012a/examples/formatSubplot.m, MATLAB, 38 lines
subscripts/CircStat2012a/examples/parseVarArgs.m, MATLAB, 39 lines
subscripts/CircStat2012a/zifredder-CircHist-05752bf/@CircHist/CircHist.m, MATLAB, 1,873 lines
subscripts/CircStat2012a/zifredder-CircHist-05752bf/@CircHist/CircHist_mh.m, MATLAB, 1,873 lines
subscripts/CircStat2012a/zifredder-CircHist-05752bf/@CircHist/exampleCircHist.m, MATLAB, 122 lines
subscripts/CircStat2012a/zifredder-CircHist-05752bf/@CircHist/exampleCircHist_live.mlx, MATLAB, not shown here
subscripts/CircStat2012a/zifredder-CircHist-05752bf/FFFlash_res_display_1ch_mh_forV6.m, MATLAB, 1 line
subscripts/CompassPlot.m, MATLAB, 217 lines
subscripts/CompassPlot_reducedN.m, MATLAB, 225 lines
subscripts/ExtractMinandMax.m, MATLAB, 143 lines
subscripts/FitGaussian2_2D.m, MATLAB, 112 lines
subscripts/Fit_Gauss.m, MATLAB, 344 lines
subscripts/Gaussian1_2D.m, MATLAB, 30 lines
subscripts/Gaussian2_2D.m, MATLAB, 19 lines
subscripts/Gaussian_Fit_C2C3.m, MATLAB, 49 lines, 2 matches
subscripts/Gaussian_Fit_Mi1.m, MATLAB, 59 lines, 2 matches
subscripts/Multcomp_Stats.m, MATLAB, 84 lines
subscripts/PlotBoxPlots_VS.m, MATLAB, 684 lines
subscripts/Plot_GaussFit_On_Av.m, MATLAB, 447 lines
subscripts/Plot_GaussFit_On_Av_Comp_Scale.m, MATLAB, 447 lines
subscripts/Plot_Polar_Plot.m, MATLAB, 131 lines
subscripts/Plot_allresp_relative.m, MATLAB, 338 lines
subscripts/Plot_allresp_relative_noaverage.m, MATLAB, 550 lines
subscripts/Plot_timeSectionSTRFC2C3.m, MATLAB, 193 lines
subscripts/Plot_timeSectionSTRFC2C3_gh.m, MATLAB, 199 lines
subscripts/Plot_timeSectionSTRFC2C3_gh_REV.m, MATLAB, 200 lines
subscripts/Plot_timeSectionSTRFMi1.m, MATLAB, 183 lines
subscripts/PolarPlot_std_new.m, MATLAB, 23 lines
subscripts/Violinplot-Matlab-master/Violin.m, MATLAB, 346 lines
subscripts/Violinplot-Matlab-master/test_cases/testviolinplot.m, MATLAB, 21 lines
subscripts/Violinplot-Matlab-master/violinplot.m, MATLAB, 127 lines
subscripts/aggregate_fffall_means10Hz_BleedThruFix_v2_CA.m, MATLAB, 135 lines
subscripts/aggregate_fffall_means10Hz_BleedThruFix_v2_mh.m, MATLAB, 103 lines
subscripts/averageDirectionVectors.m, MATLAB, 180 lines
subscripts/b2r.m, MATLAB, 133 lines
subscripts/calc_reliability.m, MATLAB, 37 lines
subscripts/darkb2r/darkb2r.m, MATLAB, 134 lines
subscripts/mean_cat_full.m, MATLAB, 22 lines
subscripts/plot_GaussFit_statistics2.m, MATLAB, 723 lines
subscripts/plot_err_patch_v2.m, MATLAB, 18 lines
LICENSE, License, 21 lines
README.md, Text, 46 lines

At the source: github.com/silieslab/C2C3_feedback_Henning

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

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;
108 scripts, each with its path and the digest of its content;
7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data availability

In vivo calcium imaging data produced for this study, as well as behavioral datasets, and the relevant analysis code are available at https://github.com/silieslab/C2C3_feedback_Henning (copy archived at Silieslab, 2026).

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 7 keywords, 9 MeSH terms, 2 funders, 93 references, 29 RRIDs.

Cite

This paper

Henning, M., Ketkar, M. D., Lüffe, T., Gohl, D. M., Clandinin, T. R., & Silies, M. (2026). Inhibitory columnar feedback neurons are involved in motion processing in Drosophila. eLife, 14, RP108529. https://doi.org/10.7554/elife.108529

BibTeX

@article{henning2026inhibitory, author = {Henning, Miriam and Ketkar, Madhura D and Lüffe, Teresa and Gohl, Daryl M and Clandinin, Thomas R and Silies, Marion}, title = {{Inhibitory columnar feedback neurons are involved in motion processing in \<i\>Drosophila\</i\>}}, journal = {eLife}, year = {2026}, month = may, volume = {14}, pages = {RP108529}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.108529}, url = {https://doi.org/10.7554/elife.108529}, pmid = {42187121}, pmcid = {PMC13211874} }

RIS

TY - JOUR AU - Henning, Miriam AU - Ketkar, Madhura D AU - Lüffe, Teresa AU - Gohl, Daryl M AU - Clandinin, Thomas R AU - Silies, Marion TI - Inhibitory columnar feedback neurons are involved in motion processing in Drosophila T2 - eLife J2 - Elife PY - 2026 DA - 2026/05/26 VL - 14 SP - RP108529 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.108529 UR - https://doi.org/10.7554/elife.108529 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.108529", "type": "article-journal", "title": "Inhibitory columnar feedback neurons are involved in motion processing in Drosophila", "container-title": "eLife", "author": [ { "family": "Henning", "given": "Miriam" }, { "family": "Ketkar", "given": "Madhura D" }, { "family": "Lüffe", "given": "Teresa" }, { "family": "Gohl", "given": "Daryl M" }, { "family": "Clandinin", "given": "Thomas R" }, { "family": "Silies", "given": "Marion" } ], "container-title-short": "Elife", "volume": "14", "page": "RP108529", "DOI": "10.7554/elife.108529", "PMID": "42187121", "PMCID": "PMC13211874", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.108529", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 26 ] ] } }

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Inhibitory columnar feedback neurons are involved in motion processing in <i>Drosophila</i>.

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Inhibitory columnar feedback neurons are involved in motion processing in <i>Drosophila</i>.