Locomotion optimizes sensory representations through a computational principle shared by rodents and primates.

Code ↔ Paper

15 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 15 matches

[1] § METHODS › Nonlinearity optimization ↔ fig2/optimize_all_gabor.ipynb, lines 655–702 · score 0.86 · mutual information, response entropy, Gaussian distribution, stimulus bin, response bin, optimal parameters
[2] § METHODS › Optimization of inhibitory connections ↔ fig4/network_interaction_analysis.ipynb, lines 341–483 · score 0.83 · enforce positivity, Pearson correlation, loss function, network interaction, gradient, subtracted
[3] § METHODS › Nonlinearity optimization ↔ fig2/compute_ori_tuning.py, lines 24–135 · score 0.74 · logistic nonlinearity, Gaussian distribution, response bin, optimal parameters, entropy, utility
[4] § METHODS › Temporal dynamics ↔ fig3/temporal_filtering_analysis.ipynb, lines 1170–1208 · score 0.73 · inverse Fourier transform, white noise, autocorrelation function, temporal filters, spectra, power
[5] § METHODS › Temporal filtering ↔ fig3/temporal_filtering_analysis.ipynb, lines 1170–1208 · score 0.69 · filtered spectra, white noise, temporal filtering, autocorrelation function, spectral, power
[6] § METHODS › Minimal model of sensing during locomotion ↔ minimal_model/agent_sim.py, lines 74–129 · score 0.69 · starting position, minimal model, agent, circle, uniform, velocity
[7] § METHODS › Orientation tuning curves ↔ fig2/optimize_all_gabor.ipynb, lines 1327–1389 · score 0.64 · stationary tuning curve, moving tuning curve, regressing, additive, Gabor filters, fit
[8] § METHODS › Minimal model of sensing during locomotion ↔ minimal_model/agent_sim_analysis.ipynb, lines 78–177 · score 0.63 · starting position, minimal model, trajectories, velocity, radius, agent
[9] § METHODS › Population coding fidelity ↔ fig2/decoding_error_decreases_gabor.ipynb, lines 415–443 · score 0.62 · decoding error, Gaussian filter, decoder, firing rate, smoothed, concatenated
[10] § METHODS › Orientation tuning curves ↔ fig2/compute_ori_tuning.py, lines 177–226 · score 0.61 · stationary tuning curve, moving tuning curve, regressing, additive, Gabor filters, fit
[11] § RESULTS › Modulation of temporal filtering ensures efficiency of sensory coding during locomotion ↔ fig3/temporal_filtering_analysis.ipynb, lines 228–257 · score 0.59 · frequency domain, temporal frequencies, temporal filter, linear, Figure 3
[12] § METHODS › Analysis of data from freely moving mice ↔ supp2/visualize_data_for_anti_mod.ipynb, lines 48–142 · score 0.58 · visual angle, Gabor filter bank, cpd, resolution
[13] § METHODS › Analysis of data from freely moving mice ↔ supp2/visualize_data_for_anti_mod_no_eye_movement_correction.ipynb, lines 35–128 · score 0.58 · visual angle, Gabor filter bank, cpd, resolution
[14] § METHODS › Surround suppression ↔ fig4/network_interaction_analysis.ipynb, lines 1678–1720 · score 0.53 · circular mask, radii, gratings, radius, Gabor filters, fitting
[15] § RESULTS › Stimulus statistics explain differential modulation of sensory coding by locomotion in rodents and primates ↔ fig5/optimize_all_gabor_species_comp.ipynb, lines 1229–1363 · score 0.51 · confidence interval, firing rate, foveal, species, optimized, modulated

Paper

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The authors' code

Jupyter notebook · 2,590 lines · 158 KB · no license · 3 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: fig3/temporal_filtering_analysis.ipynb.

Overview

Authors: Jonathan M. Gant^1,2, Wiktor F. Młynarski^1,3

ORCID iDs: Jonathan M. Gant, Wiktor F. Młynarski

Faculty of Biology, LMU, Munich, Germany
Graduate School of Systemic Neurosciences, Munich, Germany
Bernstein Center for Computational Neuroscience Munich, Munich, Germany

Institutions: Ludwig-Maximilians-Universität München (Germany); Bernstein Center for Computational Neuroscience Munich (Germany)

Journal: Science advances, volume 12, issue 35, article eaed4172

Dates: received 27 October 2025; accepted 20 July 2026; published online 28 August 2026; in print August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1126/sciadv.aed4172 · PMID 42664357 · PMCID PMC13524049 · OpenAlex W7154616600

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: cellular / molecular (subfield)

Methods: Smoothing, state filtering, decompositions, Statistics, Connectivity, Machine learning, Single-unit activity, calcium imaging

MeSH: Locomotion*, Models, Neurological*, Primates*, Rodentia*, Sensory Receptor Cells*, Animals (* major topic)

Journal subjects: Neuroscience

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Behavior modulates the activity of sensory systems in multiple ways: from gain changes in individual neurons to changing interactions in neural populations. These effects are not universal; while movement has a strong influence on sensory coding in rodents, its impact on primates is less prominent. The diversity of effects that locomotion exerts on sensory neurons, as well as disparities between species, raises questions about the existence of universal principles that may underlie sensation during behavior. We propose that sensory systems are internally modulated to match systematic changes in stimulus statistics caused by locomotion, to facilitate an accurate and efficient sensory code. We find that model neurons, adapted to stimuli recorded during movement in natural environments, predict and reproduce a broad spectrum of experimental observations in rodents and primates. This simple principle of maintaining coding efficiency across behavioral states reconciles the diversity of ways in which locomotion modulates visual coding in different animal species.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

Zenodo 20624372

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data, code, and materials availability:”

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

Tools: NumPy (31 files), h5py (21 files), Matplotlib (20 files), SciPy (19 files), scikit-learn (7 files), OpenCV (6 files), pandas (2 files), PyTorch (2 files), Numba (1 file), rpy2 (1 file), statsmodels (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

32 files

fig1/fig1_visualizations.ipynb — Jupyter, 710 lines
fig2/compute_contrast_tuning_curves.py — Python, 114 lines
fig2/compute_optimal_k_L.py — Python, 138 lines
fig2/compute_ori_tuning.py — Python, 230 lines
fig2/decoding_error_decreases_gabor.ipynb — Jupyter, 446 lines
fig2/firing_rate_vs_speed.ipynb — Jupyter, 353 lines
fig2/optimize_all_gabor.ipynb — Jupyter, 1,921 lines
fig3/horrocks_data.ipynb — Jupyter, 466 lines
fig3/temporal_filtering_analysis.ipynb — Jupyter, 2,590 lines
fig4/gen_ic_filt_resp.py — Python, 164 lines
fig4/image_patch_ica.py — Python, 98 lines
fig4/network_interaction_analysis.ipynb — Jupyter, 2,339 lines
fig4/optimize_decorr_weights_demean_relu_filters.py — Python, 171 lines
fig5/fig5_visualization.ipynb — Jupyter, 567 lines
fig5/marmoset_sf.ipynb — Jupyter, 138 lines
fig5/optimize_all_gabor_species_comp.ipynb — Jupyter, 1,833 lines
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf.py — Python, 154 lines
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf_low_sf.py — Python, 147 lines
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf_low_sf_eye_movements.py — Python, 160 lines
gen_gabor_response/optimize_nonlinearity_gaussian_analytic_fast.py — Python, 113 lines
minimal_model/agent_sim.py — Python, 166 lines
minimal_model/agent_sim_analysis.ipynb — Jupyter, 630 lines
supp1/eye_movement_analysis.ipynb — Jupyter, 389 lines
supp2/visualize_data_for_anti_mod.ipynb — Jupyter, 1,376 lines
supp2/visualize_data_for_anti_mod_no_eye_movement_correction.ipynb — Jupyter, 1,484 lines
supp3/gabor_filter_bank_new_nat_video_matched_rf_recoded_green_uv.py — Python, 179 lines
supp3/green_blue_analysis.ipynb — Jupyter, 174 lines
supp4/summary_stat_biphasic_filters.ipynb — Jupyter, 289 lines
supp5/analyze_nonlinearity_optimization_with_noise.ipynb — Jupyter, 108 lines
supp5/optimize_nonlinearity_gaussian_fast_explicit_MI_with_noise.py — Python, 102 lines
supp6/firing_rate_vs_speed_per_env.ipynb — Jupyter, 296 lines
README.md — Text, 44 lines

At the source: doi.org/10.5281/zenodo.20624372

mlynarski-group/locomotion-modulation

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 24c076f12556f265925d8408ef9f6bff8131385f, 10 June 2026

Languages: Jupyter (18), Python (13)

Size: 42 files, 31 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, environment (requirements.txt), 18 notebooks

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

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

32 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 24c076f, when its fingerprint is the one OSCR verified. How this works.

fig1/fig1_visualizations.ipynb — Jupyter, 710 lines, shown from its source
fig2/compute_contrast_tuning_curves.py — Python, 114 lines, shown from its source
fig2/compute_optimal_k_L.py — Python, 138 lines, shown from its source
fig2/compute_ori_tuning.py — Python, 230 lines, 2 matches, shown from its source
fig2/decoding_error_decreases_gabor.ipynb — Jupyter, 446 lines, 1 match, shown from its source
fig2/firing_rate_vs_speed.ipynb — Jupyter, 353 lines, shown from its source
fig2/optimize_all_gabor.ipynb — Jupyter, 1,921 lines, 2 matches, shown from its source
fig3/horrocks_data.ipynb — Jupyter, 466 lines, shown from its source
fig3/temporal_filtering_analysis.ipynb — Jupyter, 2,590 lines, 3 matches, shown from its source
fig4/gen_ic_filt_resp.py — Python, 164 lines, shown from its source
fig4/image_patch_ica.py — Python, 98 lines, shown from its source
fig4/network_interaction_analysis.ipynb — Jupyter, 2,339 lines, 2 matches, shown from its source
fig4/optimize_decorr_weights_demean_relu_filters.py — Python, 171 lines, shown from its source
fig5/fig5_visualization.ipynb — Jupyter, 567 lines, shown from its source
fig5/marmoset_sf.ipynb — Jupyter, 138 lines, shown from its source
fig5/optimize_all_gabor_species_comp.ipynb — Jupyter, 1,833 lines, 1 match, shown from its source
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf.py — Python, 154 lines, shown from its source
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf_low_sf.py — Python, 147 lines, shown from its source
gen_gabor_response/gabor_filter_bank_new_nat_video_matched_rf_low_sf_eye_movements.py — Python, 160 lines, shown from its source
gen_gabor_response/optimize_nonlinearity_gaussian_analytic_fast.py — Python, 113 lines, shown from its source
minimal_model/agent_sim.py — Python, 166 lines, 1 match, shown from its source
minimal_model/agent_sim_analysis.ipynb — Jupyter, 630 lines, 1 match, shown from its source
supp1/eye_movement_analysis.ipynb — Jupyter, 389 lines, shown from its source
supp2/visualize_data_for_anti_mod.ipynb — Jupyter, 1,376 lines, 1 match, shown from its source
supp2/visualize_data_for_anti_mod_no_eye_movement_correction.ipynb — Jupyter, 1,484 lines, 1 match, shown from its source
supp3/gabor_filter_bank_new_nat_video_matched_rf_recoded_green_uv.py — Python, 179 lines, shown from its source
supp3/green_blue_analysis.ipynb — Jupyter, 174 lines, shown from its source
supp4/summary_stat_biphasic_filters.ipynb — Jupyter, 289 lines, shown from its source
supp5/analyze_nonlinearity_optimization_with_noise.ipynb — Jupyter, 108 lines, shown from its source
supp5/optimize_nonlinearity_gaussian_fast_explicit_MI_with_noise.py — Python, 102 lines, shown from its source
supp6/firing_rate_vs_speed_per_env.ipynb — Jupyter, 296 lines, shown from its source
README.md — Text, 46 lines, shown from its source

At the source: github.com/Mlynarski-Group/locomotion-modulation/tree/v1.0.0

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
62 scripts, each with its path and the digest of its content;
15 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

doi:10.12751/g-node.14m4gq — at GIN; found in “Data, code, and materials availability:”

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. The data (natural videos) used in the analysis are available at https://doi.org/10.12751/g-node.14m4gq. The code is publicly available at https://doi.org/10.5281/zenodo.20624372. No new materials were generated in this study.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 MeSH terms, 59 references.

Cite

This paper

Gant, J. M., & Młynarski, W. F. (2026). Locomotion optimizes sensory representations through a computational principle shared by rodents and primates. Science advances, 12(35), eaed4172. https://doi.org/10.1126/sciadv.aed4172

BibTeX

@article{gant2026locomotion, author = {Gant, Jonathan M. and Młynarski, Wiktor F.}, title = {{Locomotion optimizes sensory representations through a computational principle shared by rodents and primates}}, journal = {Science advances}, year = {2026}, month = aug, volume = {12}, number = {35}, pages = {eaed4172}, publisher = {American Association for the Advancement of Science}, issn = {2375-2548}, doi = {10.1126/sciadv.aed4172}, url = {https://doi.org/10.1126/sciadv.aed4172}, pmid = {42664357}, pmcid = {PMC13524049} }

RIS

TY - JOUR AU - Gant, Jonathan M. AU - Młynarski, Wiktor F. TI - Locomotion optimizes sensory representations through a computational principle shared by rodents and primates T2 - Science advances J2 - Sci Adv PY - 2026 DA - 2026/08/28 VL - 12 IS - 35 SP - eaed4172 SN - 2375-2548 PB - American Association for the Advancement of Science DO - 10.1126/sciadv.aed4172 UR - https://doi.org/10.1126/sciadv.aed4172 LA - en ER -

CSL-JSON

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