Circuit dynamics of binocular conflict in mouse primary visual cortex.

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.

The 8 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Results › Both regular-spiking and fast-spiking units show prolonged activity with orthogonal stimuli ↔ Units analysis (Fig 2-5)/Group Files and Code/group_analysis.m, lines 106–120 · score 0.78 · 40–80 ms, 100–200 ms, early peak, 40 ms, RS, PV
[2] § Materials and methods › Method details › Electrophysiology recordings and analysis ↔ VEP analysis (Fig 1)/+process/grab_ramp_data.m, lines 1–100 · score 0.77 · notch filter, notch frequencies, detrended, locdetrend, IIR, bandwidth
[3] § Materials and methods › Method details › Single unit activity analysis ↔ Units analysis (Fig 2-5)/Group Files and Code/group_analysis.m, lines 106–120 · score 0.77 · 40–80 ms, 100–200 ms, 40 ms, SUA, RS, peak
[4] § Results › Both regular-spiking and fast-spiking units show prolonged activity with orthogonal stimuli ↔ Units analysis (Fig 2-5)/Group Files and Code/+process/get_spike_widths.m, the whole file · a weak match · score 0.67 · fast spiking, regular spiking, classified, trough, onset, 40 ms
[5] § Materials and methods › Method details › Single unit activity analysis ↔ Units analysis (Fig 2-5)/Individual Files and Code/run_animal.m, lines 128–171 · score 0.67 · high pass filter, clusters, Kilosort, binning, trough, PSTHs
[6] § Results › Both regular-spiking and fast-spiking units show prolonged activity with orthogonal stimuli ↔ Units analysis (Fig 2-5)/Group Files and Code/+process/get_spike_widths.m, the whole file · a weak match · score 0.62 · fast spiking, regular spiking, width, classified, trough, 40 ms
[7] § Results › Both regular-spiking and fast-spiking units show prolonged activity with orthogonal stimuli ↔ Units analysis (Fig 2-5)/Group Files and Code/+view/plot_all_SUA.m, the whole file · a weak match · score 0.59 · firing rates, 80 ms, location, 40 ms, L6, L5
[8] § Results › Both regular-spiking and fast-spiking units show prolonged activity with orthogonal stimuli ↔ Units analysis (Fig 2-5)/Group Files and Code/+view/plot_all_SUA.m, the whole file · a weak match · score 0.54 · Firing rates, 80 ms, 40 ms, L6, L5, SEM

Paper

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The authors' code

MATLAB · 125 lines · 2.8 KB · MIT · 2 matches

%% Load data
mice = [{'DM988'}, {'DM990'}, {'DM991'}, {'DM992'}, {'DM993'}, {'DM994'}, {'DM1005'}, {'DM1006'}, {'DM1007'}, {'DM1009'}, {'DM1010'}];
data_path = '\\bearlab-s2.mit.edu\bearshare\Daniel#\Experiments#\Units\Suppression\Individual Files and Code\';
layer_borders = [300, 60, -80, -260, -460];
group_struct = import.load_mouse_data(mice, data_path);
SUA_struct = import.load_SUA_data(mice, data_path);
%% Organize data
[VEP, VEP_stats, depth_VEPs] = process.VEP_analysis(group_struct);
[CSD, MUA] = process.CSD_MUA_analysis(group_struct);
zMUA = process.zscore_MUA(MUA);
%% Plot VEPs, CSDs, and MUA
view.view_VEPs(VEP, VEP_stats);
view.view_VEPs_by_layer(depth_VEPs)
view.view_group_heatmap(CSD, "CSD", layer_borders)
view.view_group_heatmap(MUA, "MUA", layer_borders)
view.view_group_heatmap(zMUA, "zMUA", layer_borders)
MUA_time = 1:25;
view.view_MUA_by_depth(MUA, layer_borders, MUA_time)
%% Compare with monocular condition
CI = 99; % percent confidence interval
view.view_group_heatmap_compare(CSD, "CSD", layer_borders, CI);
view.view_group_heatmap_compare(MUA, "MUA", layer_borders, CI);
view.view_group_heatmap_compare(zMUA, "zMUA", layer_borders, CI);
%% Plot traces for CSDs and MUA
cortex_borders = [300, -460];
x = view.view_group_traces(zMUA, "MUA", cortex_borders);
cortex_borders = [300, -460];
x = view.view_group_traces(MUA, "MUA", cortex_borders);
%% Organize SUA data
good_cells_only = false;
[SUA, SUA_raster, unit_depths] = process.SUA_analysis(SUA_struct, layer_borders, good_cells_only);
%% Plot SUA
zscore = true;
smooth_data = true;
plot_errorbars = false;
view.plot_layer_SUA(SUA, zscore, smooth_data, plot_errorbars)
view.plot_all_SUA(SUA, zscore, smooth_data, plot_errorbars)
%% Plot single unit PSTHs
zscore = true;
smooth_data = true;
view.plot_SUA_PSTH_by_layer(SUA, zscore, smooth_data)
%% Get rasters for SUA
view.plot_SUA_raster(SUA_raster.pyr.L23, [])
view.plot_SUA_raster(SUA_raster.pyr.L4, [])
view.plot_SUA_raster(SUA_raster.pyr.L5, [])
view.plot_SUA_raster(SUA_raster.pyr.L6, [])
view.plot_SUA_raster(SUA_raster.pv.L23, [])
view.plot_SUA_raster(SUA_raster.pv.L4, [])
view.plot_SUA_raster(SUA_raster.pv.L5, [])
view.plot_SUA_raster(SUA_raster.pv.L6, [])
%% Get SUA to run stats
% Get SUA for each layer for the early peak (40-80 ms) and late peak (100-200 ms)
output_format = 1; % 1 or 2
zscore = false;
SUA_stats = process.get_SUA_stats(SUA, output_format, zscore)
view.plot_SUA_cdf(SUA_stats.pyr.all.early_peak, 'RS early peak');
view.plot_SUA_cdf(SUA_stats.pyr.all.late_peak, 'RS late peak');
view.plot_SUA_cdf(SUA_stats.pv.all.early_peak, 'FS early peak');
view.plot_SUA_cdf(SUA_stats.pv.all.late_peak, 'FS late peak');

group_analysis.m at commit 17469a5, under MIT · at the source

Overview

Authors: Daniel P. Montgomery^1,2,3, Daniel A. Bowen^2,4, Jin Wu⁵, Mark F. Bear², Eric D. Gaier^2,4,6

Institutions: Children's National (United States); Massachusetts Institute of Technology (United States); Boston Children's Hospital (United States); Brandeis University (United States); Harvard University (United States)

Journal: Frontiers in systems neuroscience, volume 20, article 1786396

Dates: received 12 January 2026; accepted 29 April 2026; published online 29 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3389/fnsys.2026.1786396 · PMID 42292979 · PMCID PMC13260517 · OpenAlex W7162793557

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), systems (subfield)

Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: binocular rivalry, binocular vision, interocular suppression, primary visual cortex, somatostatin interneurons

Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: cited by 1 paper (Europe PMC); 52 references in the paper

Research resources: Cre mice RRID:IMSR_JAX:005628, SOM-Cre mice RRID:IMSR_JAX:018973, CaMK2a-tTA mice RRID:IMSR_JAX:024108, RRID:SCR_002798

Abstract

Binocular vision requires that the brain integrate input from both eyes to form a unified percept. Small interocular differences support depth perception (stereopsis), while larger disparities can cause diplopia or binocular rivalry. The neural mechanisms by which early visual circuits process concordant versus conflicting binocular signals remain incompletely understood. Here, we used visually evoked potentials (VEPs), unit recordings, and 2-photon calcium imaging in the binocular region of mouse primary visual cortex (bV1) to examine how distinct forms of binocular disparity engage local circuits. We found that interocular phase disparities reduced VEP magnitude through decreased neuronal firing early in the response (40–80 ms after stimulus onset). Orientation disparities also decreased VEP magnitude, but via increased firing later in the response (100–200 ms). This late activity was enhanced in both regular-spiking (putative excitatory) and fast-spiking (putative parvalbumin-positive inhibitory) units. In contrast, calcium imaging revealed that somatostatin-positive interneurons were suppressed during orientation conflict. These findings suggest that phase differences suppress bV1 responses via feedforward mechanisms, while orientation disparities prolong activity through a process associated with suppression of somatostatin-positive interneurons. Our results reveal cell-type specific circuit mechanisms engaged by different forms of binocular conflict and provide a foundation for mechanistic investigations of perceptual suppression and rivalry.

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 8 matches between paragraphs and lines of code.

danielmontgomery7/suppression-analysis-code

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 17469a5f0ab2ddefa01548e606abea6432be1f27, 6 February 2025

Languages: MATLAB (125)

Size: 139 files, 125 scripts

Software Heritage: not archived

Found in: the end of the paper

Holds: README, license file

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

Tools: Signal Processing Toolbox (3 files), Chronux (2 files), Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file)

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

28 September 2026: the link answers

127 files

2P analysis (Fig 6)/+analyze/IOS_analyze_wrapper.m, MATLAB, 61 lines
2P analysis (Fig 6)/+analyze/analyze_wrapper.m, MATLAB, 69 lines
2P analysis (Fig 6)/+analyze/calculate_F0.m, MATLAB, 70 lines
2P analysis (Fig 6)/+analyze/calculate_dFF.m, MATLAB, 42 lines
2P analysis (Fig 6)/+analyze/extract_traces.m, MATLAB, 24 lines
2P analysis (Fig 6)/+analyze/get_block_timing.m, MATLAB, 63 lines
2P analysis (Fig 6)/+analyze/organize_block_data.m, MATLAB, 84 lines
2P analysis (Fig 6)/+analyze/separate_traces.m, MATLAB, 24 lines
2P analysis (Fig 6)/+describe_experiments/IOS_info.m, MATLAB, 310 lines
2P analysis (Fig 6)/+describe_experiments/L23_OD_info.m, MATLAB, 171 lines
2P analysis (Fig 6)/+describe_experiments/PV_TTX_info.m, MATLAB, 135 lines
2P analysis (Fig 6)/+describe_experiments/SOM_IOS_info.m, MATLAB, 116 lines
2P analysis (Fig 6)/+describe_experiments/blank_experiment_info.m, MATLAB, 71 lines
2P analysis (Fig 6)/+describe_experiments/group_assignments.m, MATLAB, 73 lines
2P analysis (Fig 6)/+import/IOS_import_wrapper.m, MATLAB, 66 lines
2P analysis (Fig 6)/+import/OD_get_filenames.m, MATLAB, 67 lines
2P analysis (Fig 6)/+import/OD_import_wrapper.m, MATLAB, 64 lines
2P analysis (Fig 6)/+import/get_filenames.m, MATLAB, 63 lines
2P analysis (Fig 6)/+import/import_SRP_params.m, MATLAB, 45 lines
2P analysis (Fig 6)/+import/import_csv.m, MATLAB, 90 lines
2P analysis (Fig 6)/+import/import_experiment_params.m, MATLAB, 40 lines
2P analysis (Fig 6)/+import/import_suite2p_traces.m, MATLAB, 29 lines
2P analysis (Fig 6)/+import/import_xml.m, MATLAB, 31 lines
2P analysis (Fig 6)/+import/update_rois.m, MATLAB, 16 lines
2P analysis (Fig 6)/+organize_data/get_IOS_data.m, MATLAB, 84 lines
2P analysis (Fig 6)/+organize_data/get_phaseoffset_data.m, MATLAB, 58 lines
2P analysis (Fig 6)/+plot/IOS_mouse_data.m, MATLAB, 143 lines
2P analysis (Fig 6)/+plot/SOM_IOS_mouse_data.m, MATLAB, 88 lines
2P analysis (Fig 6)/+stats/contra_bias_idx.m, MATLAB, 36 lines
2P analysis (Fig 6)/+stats/nph_boot.m, MATLAB, 85 lines
2P analysis (Fig 6)/+stats/nph_boot_diff.m, MATLAB, 146 lines
2P analysis (Fig 6)/PC_analysis.m, MATLAB, 211 lines
2P analysis (Fig 6)/SOM_analysis.m, MATLAB, 105 lines
2P analysis (Fig 6)/functions/ROIMatchPub-master/RoiMatchHelp.m, MATLAB, 34 lines
2P analysis (Fig 6)/functions/ROIMatchPub-master/roiMatchPub.m, MATLAB, 610 lines
2P analysis (Fig 6)/functions/catstruct.m, MATLAB, 153 lines
2P analysis (Fig 6)/functions/deriv.m, MATLAB, 7 lines
2P analysis (Fig 6)/functions/find_closest_value.m, MATLAB, 36 lines
2P analysis (Fig 6)/functions/hline.m, MATLAB, 107 lines
2P analysis (Fig 6)/functions/plot_areaerrorbar.m, MATLAB, 75 lines
2P analysis (Fig 6)/functions/readNPY.m, MATLAB, 37 lines
2P analysis (Fig 6)/functions/readNPYheader.m, MATLAB, 69 lines
2P analysis (Fig 6)/functions/shadedErrorBar.m, MATLAB, 254 lines
2P analysis (Fig 6)/functions/shadedErrorBar_old.m, MATLAB, 254 lines
2P analysis (Fig 6)/functions/vline.m, MATLAB, 107 lines
2P analysis (Fig 6)/functions/xml2struct.m, MATLAB, 183 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/get_chan_pos_string.m, MATLAB, 3 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/get_cleaned_fieldnames.m, MATLAB, 11 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/get_positions_from_names.m, MATLAB, 12 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/importfile.m, MATLAB, 11 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/interpolate_bad_chan.m, MATLAB, 19 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/name2num.m, MATLAB, 4 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/plotSpikeRaster.m, MATLAB, 515 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/redblue.m, MATLAB, 46 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/scalebar.m, MATLAB, 271 lines
Units analysis (Fig 2-5)/Group Files and Code/+helper/tsvread.m, MATLAB, 87 lines
Units analysis (Fig 2-5)/Group Files and Code/+import/load_SUA_data.m, MATLAB, 15 lines
Units analysis (Fig 2-5)/Group Files and Code/+import/load_mouse_data.m, MATLAB, 26 lines
Units analysis (Fig 2-5)/Group Files and Code/+import/load_mouse_data_stat.m, MATLAB, 18 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/CSD_MUA_analysis.m, MATLAB, 39 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/SUA_analysis.m, MATLAB, 87 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/SUA_find_good_cells.m, MATLAB, 35 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/VEP_analysis.m, MATLAB, 40 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/get_SUA_stats.m, MATLAB, 117 lines
Units analysis (Fig 2-5)/Group Files and Code/+process/get_spike_widths.m, MATLAB, 103 lines, 2 matches
Units analysis (Fig 2-5)/Group Files and Code/+process/zscore_MUA.m, MATLAB, 28 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plotSpikeRaster.m, MATLAB, 515 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_SUA_PSTH.m, MATLAB, 123 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_SUA_PSTH_by_layer.m, MATLAB, 76 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_SUA_cdf.m, MATLAB, 23 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_SUA_raster.m, MATLAB, 76 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_all_SUA.m, MATLAB, 88 lines, 2 matches
Units analysis (Fig 2-5)/Group Files and Code/+view/plot_layer_SUA.m, MATLAB, 90 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_MUA_by_depth.m, MATLAB, 76 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_VEPs.m, MATLAB, 54 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_VEPs_by_layer.m, MATLAB, 18 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_group_heatmap.m, MATLAB, 72 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_group_heatmap_compare.m, MATLAB, 124 lines
Units analysis (Fig 2-5)/Group Files and Code/+view/view_group_traces.m, MATLAB, 91 lines
Units analysis (Fig 2-5)/Group Files and Code/group_analysis.m, MATLAB, 125 lines, 2 matches
Units analysis (Fig 2-5)/Individual Files and Code/run_animal.m, MATLAB, 185 lines, 1 match
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/get_chan_pos_string.m, MATLAB, 3 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/get_cleaned_fieldnames.m, MATLAB, 11 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/get_positions_from_names.m, MATLAB, 12 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/importfile.m, MATLAB, 11 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/interpolate_bad_chan.m, MATLAB, 19 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/name2num.m, MATLAB, 4 lines
Units analysis (Fig 2-5)/Individual Files and Code/shared/+helper/plotSpikeRaster.m, MATLAB, 515 lines
VEP analysis (Fig 1)/+group/create_famnov_spect_figure.m, MATLAB, 225 lines
VEP analysis (Fig 1)/+group/create_pseudo_spectrum_figure.m, MATLAB, 292 lines
VEP analysis (Fig 1)/+group/get_summary_stats.m, MATLAB, 88 lines
VEP analysis (Fig 1)/+group/grab_group_data.m, MATLAB, 62 lines
VEP analysis (Fig 1)/+group/view_group_data.m, MATLAB, 334 lines
VEP analysis (Fig 1)/+import/describe_experiments.m, MATLAB, 145 lines
VEP analysis (Fig 1)/+import/extract_plx_file_data.m, MATLAB, 34 lines
VEP analysis (Fig 1)/+import/import_plx_data.m, MATLAB, 46 lines
VEP analysis (Fig 1)/+import/update_data_assignments.m, MATLAB, 842 lines
VEP analysis (Fig 1)/+process/grab_ramp_data.m, MATLAB, 160 lines, 1 match
VEP analysis (Fig 1)/+process/grab_spect_data.m, MATLAB, 60 lines
VEP analysis (Fig 1)/+process/specify_good_hemis.m, MATLAB, 1,343 lines
VEP analysis (Fig 1)/+process/specify_good_hemis_RHs.m, MATLAB, 1,178 lines
VEP analysis (Fig 1)/+view/view_vep_data.m, MATLAB, 74 lines
VEP analysis (Fig 1)/VEP_analysis.m, MATLAB, 336 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_downsample_rate.m, MATLAB, 15 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_downsample_spikes.m, MATLAB, 29 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_event_indices.m, MATLAB, 38 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_intan_digital_information.m, MATLAB, 20 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_intan_event_information.m, MATLAB, 73 lines
VEP analysis (Fig 1)/helper_functions/import/calculate_threshold_spikes.m, MATLAB, 39 lines
VEP analysis (Fig 1)/helper_functions/import/create_intan_event_dictionary.m, MATLAB, 37 lines
VEP analysis (Fig 1)/helper_functions/import/extract_filtered_intan_file_data.m, MATLAB, 26 lines
VEP analysis (Fig 1)/helper_functions/import/extract_high_pass_filtered_data.m, MATLAB, 66 lines
VEP analysis (Fig 1)/helper_functions/import/extract_intan_file_data.m, MATLAB, 17 lines
VEP analysis (Fig 1)/helper_functions/import/extract_plx_file_data.m, MATLAB, 35 lines
VEP analysis (Fig 1)/helper_functions/import/import_intan_data_vector.m, MATLAB, 17 lines
VEP analysis (Fig 1)/helper_functions/import/import_intan_digital_vector.m, MATLAB, 15 lines
VEP analysis (Fig 1)/helper_functions/import/import_intan_event_vector.m, MATLAB, 25 lines
VEP analysis (Fig 1)/helper_functions/import/import_intan_time_vector.m, MATLAB, 27 lines
VEP analysis (Fig 1)/objects/data/dataContainerClass.m, MATLAB, 33 lines
VEP analysis (Fig 1)/objects/data/extracellularDataClass.m, MATLAB, 100 lines
VEP analysis (Fig 1)/objects/import/digitalInfoClass.m, MATLAB, 65 lines
VEP analysis (Fig 1)/objects/import/eventInfoClass.m, MATLAB, 68 lines
VEP analysis (Fig 1)/objects/import/fileInfoAbstractClass.m, MATLAB, 46 lines
VEP analysis (Fig 1)/objects/import/intanFileInfoClass.m, MATLAB, 118 lines
VEP analysis (Fig 1)/objects/import/plxFileInfoClass.m, MATLAB, 78 lines
LICENSE, License, 21 lines
README.md, Text, 2 lines

At the source: github.com/danielmontgomery7/suppression-analysis-code

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.

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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;
125 scripts, each with its path and the digest of its content;
8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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

Data and code availability

The code used to analyze visually evoked potentials are available on Github.2 The datasets generated during this study have not been deposited in a public repository but are available from the corresponding author on request.

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, 5 authors, 5 keywords, 52 references, 4 RRIDs.

Cite

This paper

Montgomery, D. P., Bowen, D. A., Wu, J., Bear, M. F., & Gaier, E. D. (2026). Circuit dynamics of binocular conflict in mouse primary visual cortex. Frontiers in systems neuroscience, 20, 1786396. https://doi.org/10.3389/fnsys.2026.1786396

BibTeX

@article{montgomery2026circuit, author = {Montgomery, Daniel P. and Bowen, Daniel A. and Wu, Jin and Bear, Mark F. and Gaier, Eric D.}, title = {{Circuit dynamics of binocular conflict in mouse primary visual cortex}}, journal = {Frontiers in systems neuroscience}, year = {2026}, month = may, volume = {20}, pages = {1786396}, publisher = {Frontiers Media SA}, issn = {1662-5137}, doi = {10.3389/fnsys.2026.1786396}, url = {https://doi.org/10.3389/fnsys.2026.1786396}, pmid = {42292979}, pmcid = {PMC13260517} }

RIS

TY - JOUR AU - Montgomery, Daniel P. AU - Bowen, Daniel A. AU - Wu, Jin AU - Bear, Mark F. AU - Gaier, Eric D. TI - Circuit dynamics of binocular conflict in mouse primary visual cortex T2 - Frontiers in systems neuroscience J2 - Front Syst Neurosci PY - 2026 DA - 2026/05/29 VL - 20 SP - 1786396 SN - 1662-5137 PB - Frontiers Media SA DO - 10.3389/fnsys.2026.1786396 UR - https://doi.org/10.3389/fnsys.2026.1786396 LA - en ER -

CSL-JSON

{ "id": "10.3389/fnsys.2026.1786396", "type": "article-journal", "title": "Circuit dynamics of binocular conflict in mouse primary visual cortex", "container-title": "Frontiers in systems neuroscience", "author": [ { "family": "Montgomery", "given": "Daniel P." }, { "family": "Bowen", "given": "Daniel A." }, { "family": "Wu", "given": "Jin" }, { "family": "Bear", "given": "Mark F." }, { "family": "Gaier", "given": "Eric D." } ], "container-title-short": "Front Syst Neurosci", "volume": "20", "page": "1786396", "DOI": "10.3389/fnsys.2026.1786396", "PMID": "42292979", "PMCID": "PMC13260517", "ISSN": "1662-5137", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fnsys.2026.1786396", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 29 ] ] } }

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[6] doi:10.1523/eneuro.0417-25.2026 [code]: Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice.
Journal: eNeuro
In common: Chronux, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, mouse, 1 reference
[7] doi:10.1523/jneurosci.2001-25.2026 [code]: Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: Chronux, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, systems, 1 reference
[8] doi:10.1038/s41593-026-02357-2 [code]: Experience reorganizes content-specific memory traces in macaques.
Journal: Nature neuroscience
In common: Chronux, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, 1 reference
[9] doi:10.1038/s41467-026-72620-4 [code]: Grid maps are disrupted after large-scale arena expansion and recover with experience.
Journal: Nature communications
In common: Chronux, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, systems, mouse
[10] doi:10.1038/s41467-026-73106-z [code]: Respiratory pauses highlight sleep architecture in mice.
Journal: Nature communications
In common: Chronux, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, EEG, mouse

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Circuit dynamics of binocular conflict in mouse primary visual cortex.

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