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Principles of gamma synchrony predict figure-ground perception in texture stimuli.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Synchrony principles govern static figure-ground perception ↔ scripts/simulation/parameter_exploration.py, lines 140–210 · score 0.83 · parameter space exploration, min max normalization, weighted Jaccard, V1 model, behavioral Arnold tongue, model parameter
  2. [2] § Methods › Behavioral experiments › Tasks and procedure ↔ experiment.m, lines 239–388 · score 0.69 · inter trial interval, fixation window, gaze, pressing
  3. [3] § Results › Plasticity-induced changes in synchrony quantitatively predict perceptual learning ↔ scripts/plotting/figure_five.py, lines 316–375 · score 0.68 · min max normalized, noise ceiling, confidence intervals, bars, mixed, Arnold tongue
  4. [4] § Methods › Behavioral experiments › Tasks and procedure ↔ setupEyeTracker.m, the whole file · a weak match · score 0.68 · eye tracker, Bit, Eyelink, Windows, resolution, Fixation
  5. [5] § Methods › Oscillator model of V1 › Intrinsic frequency ↔ src/v1_model.py, lines 101–146 · score 0.66 · threshold linear, receptive field, diameter, Gaussian, eccentricity, pixel
  6. [6] § Results › Plasticity-induced changes in synchrony quantitatively predict perceptual learning ↔ scripts/plotting/figure_five.py, lines 22–58 · score 0.61 · noise ceiling, weighted Jaccard, behavioral Arnold tongue, correlations, model
  7. [7] § Results › Synchrony principles govern static figure-ground perception ↔ notebooks/statistics_session1.ipynb, lines 95–102 · score 0.61 · frequency detuning, human ability, determine synchrony, coupled oscillators, texture stimuli, coupling strength
  8. [8] § Methods › Oscillator model of V1 › Adaptive coupling ↔ scripts/simulation/crossval_estimation.py, lines 206–274 · score 0.61 · cross validation, weighted Jaccard, coarse, synchrony, model
  9. [9] § Methods › Behavioral experiments › Stimuli ↔ src/model_utils.py, lines 52–81 · score 0.60 · polar angle, visual field, eccentricity
  10. [10] § Results › Synchrony principles govern static figure-ground perception ↔ scripts/simulation/crossval_prediction.py, lines 156–253 · score 0.58 · min max normalization, weighted Jaccard, diagonally, optimal, Arnold tongue, correlations
  11. [11] § Results ↔ src/model_utils.py, lines 52–81 · score 0.57 · complex logarithmic, visual field, transformation, eccentricity, cortical, space
  12. [12] § Results › Plasticity-induced changes in synchrony quantitatively predict perceptual learning ↔ scripts/simulation/crossval_estimation.py, lines 322–367 · score 0.55 · cross validation, behavioral Arnold tongue, optimizing, fold, weighted, fitting
  13. [13] § Results ↔ experiment.m, lines 31–38 · score 0.53 · Eye tracking, fixation point, deviation, blocks, stimuli
  14. [14] § Results › Synchrony principles govern static figure-ground perception ↔ notebooks/statistics_session1.ipynb, lines 65–93 · score 0.52 · hierarchical logistic regression, model synchrony
  15. [15] § Results ↔ scripts/simulation/crossval_estimation.py, lines 206–274 · score 0.52 · cross validation, upper bound, locking, weighted, simulated, stimuli
  16. [16] § Results › Synchrony principles govern static figure-ground perception ↔ notebooks/statistics_session1.ipynb, lines 65–93 · score 0.51 · hierarchical logistic regression, statistical model, coarseness, Synchrony

Paper

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The authors' code

Jupyter notebook · 279 lines · 8 KB · MIT · 3 matches

  1. # %% [markdown]
  2. # ### Setup
  4. # %%
  5. # Add the parent directory of the current working directory to the Python path at runtime.
  6. # In order to import modules from the src directory.
  7. import os
  8. import sys
  10. current_dir = os.getcwd()
  11. parent_dir = os.path.dirname(current_dir)
  12. sys.path.insert(0, parent_dir)
  14. # %%
  15. import numpy as np
  16. import pandas as pd
  17. import bambi as bmb
  18. import arviz as az
  20. from prettytable import PrettyTable
  21. from sklearn.metrics import r2_score
  23. from src.stat_utils import *
  24. from src.anl_utils import load_data, get_session_data
  26. # %% [markdown]
  27. # ### Load and prepare data
  29. # %%
  30. sim_results_folder = '../results/simulation'
  31. data_folder = '../data'
  32. sync_at_file = os.path.join(sim_results_folder, 'first_session_arnold_tongues.npy')
  33. effective_fr_file = os.path.join(sim_results_folder, 'first_session_effective_firing_rates.npy')
  34. emp_at_file = os.path.join(data_folder, 'Experiment.csv')
  36. # %%
  37. # Load the simulations results and the empirical data
  38. sync_results = np.load(sync_at_file)
  39. sync_results_vector = sync_results.mean(axis=0).flatten()
  42. effective_results = np.load(effective_fr_file)
  43. effective_vector = effective_results.mean(axis=0).flatten()
  44. reference = effective_results[:,:,-1]
  45. delta_results = effective_results - np.expand_dims(reference, 2)
  46. delta_vector = delta_results.mean(axis=0).flatten()
  49. # Get the data for the first session
  50. data = load_data(emp_at_file)
  51. data = get_session_data(data, 1)
  54. # Map synchrony values to each condition in DataFrame
  55. data['Synchrony'] = data['Condition'].apply(lambda x: sync_results_vector[x-1])
  56. data['EffectiveFiring'] = data['Condition'].apply(lambda x: effective_vector[x-1])
  57. data['DeltaFiring'] = data['Condition'].apply(lambda x: delta_vector[x-1])
  59. data_valid = data[data['ContrastHeterogeneity'] != 1].copy()
  61. # Z-score relevant columns
  62. data = zscore_data(data, ['ContrastHeterogeneity', 'GridCoarseness', 'Synchrony', 'EffectiveFiring'])
  63. data_valid = zscore_data(data_valid, ['Synchrony', 'EffectiveFiring', 'DeltaFiring'])
  65. # %% [markdown]
  66. # ### Define statistical models
  68. # %%
  69. # Features-only hierarchical logistic regression
  70. model_features = bmb.Model(
  71. "Correct ~ 1 + ContrastHeterogeneity * GridCoarseness + (1 + ContrastHeterogeneity * GridCoarseness|SubjectID)",
  72. data=data,
  73. family="bernoulli"
  74. )
  76. # Model synchrony (mechanism)
  77. model_sync = bmb.Model(
  78. "Correct ~ 1 + Synchrony + (1 + Synchrony | SubjectID)",
  79. data=data,
  80. family="bernoulli"
  81. )
  83. model_effective = bmb.Model(
  84. "Correct ~ 1 + EffectiveFiring + (1 + EffectiveFiring | SubjectID)",
  85. data=data,
  86. family="bernoulli"
  87. )
  89. model_delta = bmb.Model(
  90. "Correct ~ 1 + DeltaFiring + (1 + DeltaFiring | SubjectID)",
  91. data=data_valid,
  92. family="bernoulli"
  93. )
  95. # %% [markdown]
  96. # Are the factors that determine synchrony among coupled oscillators (frequency detuning and coupling strength) predictive of human ability to segregate a rectangular figure from its background in texture stimuli?
  98. # %%
  99. idata_features = model_features.fit(
  100. draws=2000, tune=2000, target_accept=0.95,
  101. idata_kwargs={"log_likelihood": True}, progressbar=False
  102. )
  104. # %%
  105. predictors = ["ContrastHeterogeneity", "GridCoarseness", "ContrastHeterogeneity:GridCoarseness"]
  106. directions = ['less', 'less', 'greater']
  108. posterior = posterior_table(idata_features, predictors, directions)
  110. odds_ratios = OR_table(idata_features, predictors)
  112. print("One-sided posterior probabilities:")
  113. print(posterior)
  114. print("\nOdds ratios:")
  115. print(odds_ratios)
  117. az.summary(idata_features, var_names=predictors, hdi_prob=0.95)
  119. # %% [markdown]
  120. # Does the synchronization behavior of a biophysical model of V1 predict human ability to segregate a rectangular figure from its background in texture stimuli?
  122. # %%
  123. idata_sync = model_sync.fit(
  124. draws=2000, tune=2000, target_accept=0.95,
  125. idata_kwargs={"log_likelihood": True}, progressbar=False
  126. )
  128. # %%
  129. predictors = ["Synchrony"]
  130. directions = ['greater']
  132. posterior = posterior_table(idata_sync, predictors, directions)
  133. odds_ratios = OR_table(idata_sync, predictors)
  135. print("One-sided posterior probabilities:")
  136. print(posterior)
  137. print("\nOdds ratios:")
  138. print(odds_ratios)
  140. az.summary(idata_sync, var_names=predictors, hdi_prob=0.95)
  142. # %% [markdown]
  143. # Does firing rate explain figure-ground perception? Does it do equally well as synchrony?
  144. #
  145. # Step 1: Does pure firing rate in the figure explain figure-ground perception?
  147. # %%
  148. idata_effective = model_effective.fit(
  149. draws=2000, tune=2000, target_accept=0.95,
  150. idata_kwargs={"log_likelihood": True}, progressbar=False
  151. )
  153. # %%
  154. predictors = ["EffectiveFiring"]
  155. directions = ['greater']
  157. posterior = posterior_table(idata_effective, predictors, directions)
  158. odds_ratios = OR_table(idata_effective, predictors)
  160. print("One-sided posterior probabilities:")
  161. print(posterior)
  162. print("\nOdds ratios:")
  163. print(odds_ratios)
  165. az.summary(idata_effective, var_names=predictors, hdi_prob=0.95)
  167. # %%
  168. from scipy.special import expit
  170. post = az.extract(idata_effective, var_names=["Intercept", "EffectiveFiring"], combined=True)
  172. b0 = np.asarray(post["Intercept"]).ravel() # population intercept draws
  173. b_firing = np.asarray(post["EffectiveFiring"]).ravel() # population slope draws (per 1 SD if you z-scored)
  176. # Probability change for a 1 SD increase in Effective Firing Rate
  177. p0 = expit(b0)
  178. p1 = expit(b0 + b_firing) #
  179. delta_p = p1 - p0
  181. # Summaries
  182. h = az.hdi(delta_p, hdi_prob=0.95)
  183. out = {
  184. "mean_Δp": float(delta_p.mean()),
  185. "HDI_95%_low": float(h[0]),
  186. "HDI_95%_high": float(h[1]),
  187. "P(Δp>0)": float((delta_p > 0).mean()),
  188. "P(|Δp|<0.02)": float((np.abs(delta_p) < 0.02).mean())
  189. }
  190. print(out)
  193. # %% [markdown]
  194. # Does the difference in firing between figure and background explain figure-ground perception?
  195. # This assumes some additional downstream mechanisms that compares firing rates in different stimulus regions (not explicitly modeled)
  197. # %%
  198. idata_delta = model_delta.fit(
  199. draws=2000, tune=2000, target_accept=0.95,
  200. idata_kwargs={"log_likelihood": True}, progressbar=False
  201. )
  203. # %%
  204. predictors = ["DeltaFiring"]
  205. directions = ['less']
  207. posterior = posterior_table(idata_delta, predictors, directions)
  208. odds_ratios = OR_table(idata_delta, predictors)
  210. print("One-sided posterior probabilities:")
  211. print(posterior)
  212. print("\nOdds ratios:")
  213. print(odds_ratios)
  215. az.summary(idata_delta, var_names=predictors, hdi_prob=0.95)
  217. # %% [markdown]
  218. # Compare firing rate difference to synchrony. First refit synchrony model only on valid part of data.
  220. # %%
  221. model_sync_valid = bmb.Model(
  222. "Correct ~ 1 + Synchrony + (1 + Synchrony | SubjectID)",
  223. data=data_valid,
  224. family="bernoulli"
  225. )
  227. idata_sync_valid = model_sync_valid.fit(
  228. draws=2000, tune=2000, target_accept=0.95,
  229. idata_kwargs={"log_likelihood": True}, progressbar=False
  230. )
  232. # %%
  233. model_effective_valid = bmb.Model(
  234. "Correct ~ 1 + EffectiveFiring + (1 + EffectiveFiring | SubjectID)",
  235. data=data_valid,
  236. family="bernoulli"
  237. )
  239. idata_effective_valid = model_effective_valid.fit(
  240. draws=2000, tune=2000, target_accept=0.95,
  241. idata_kwargs={"log_likelihood": True}, progressbar=False
  242. )
  244. # %%
  245. # Compare models (LOO)
  246. az.compare({
  247. "synchrony": idata_sync_valid,
  248. "effective firing rate": idata_effective_valid,
  249. "delta firing rate": idata_delta,
  250. }, method="BB-pseudo-BMA")
  252. # %% [markdown]
  253. # ### Design Analysis
  255. # %%
  256. rng = np.random.default_rng(1709026616) # Seed for reproducibility
  257. num_simulations = 50
  258. num_subjects_list = [4, 6, 8, 10]
  260. num_available_draws = len(az.extract(idata_features, var_names=["Intercept"]).to_dataframe())
  262. results = []
  263. for num_subjects in num_subjects_list:
  264. for sim in range(num_simulations):
  265. draw_index = rng.integers(num_available_draws)
  266. df_simulated, true_betas = simulate_dataset_from_draw(
  267. idata_features, data, draw_index, num_subjects, rng=rng
  268. )
  269. sim_result = analyze_simulated(df_simulated, true_betas)
  270. sim_result["num_subjects"] = num_subjects
  271. sim_result["simulation"] = sim
  272. results.append(sim_result)
  274. results_df = pd.DataFrame(results)
  276. # %%
  277. summary = summarize_design_analysis(results_df)
  279. print(summary)

statistics_session1.ipynb at commit 127ff3a, under MIT · at the source

Overview

Authors: Maryam Karimian1,2,3,4, Mark Jonathan Roberts1,5, Peter De Weerd1,3,5, Mario Senden1,5
ORCID iDs: Maryam Karimian, Mark Jonathan Roberts, Peter De Weerd, Mario Senden
  1. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience Maastricht University Maastricht Netherlands
  2. Science of Intelligence, Research Cluster of Excellence Berlin Germany
  3. Maastricht Centre for Systems Biology (MaCSBio), Maastricht University Maastricht Netherlands
  4. Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin Berlin Germany
  5. Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University Maastricht Netherlands
Institutions: Maastricht University (Netherlands); Humboldt-Universität zu Berlin (Germany)
Journal: eLife, volume 14, article RP105482
Dates: published online 10 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.105482 · PMID 41961073 · PMCID PMC13068434 · OpenAlex W4411605694
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Machine learning, Connectivity, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: gamma synchrony, figure-ground segregation, texture stimuli, weakly coupled oscillators, visual cortex, Human
MeSH: Gamma Rhythm*, Visual Cortex*, Visual Perception*, Humans, Photic Stimulation (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 112 references in the paper

Abstract

Gamma synchrony is ubiquitous in visual cortex, but whether it contributes to perceptual grouping remains contentious based on observations that gamma frequency is not consistent across stimulus features and that gamma synchrony depends on distances between image elements. These stimulus dependencies have been argued to challenge the idea that the visual system groups image elements by synchronizing the neural assemblies that encode them. Here, we argue instead that these dependencies may shape synchrony in perceptually meaningful ways. Indeed, according to the theory of weakly coupled oscillators (TWCO), synchrony-based grouping mechanisms require stimulus dependence. Synchronization among coupled oscillators depends on frequency dissimilarity and coupling strength, which in early visual cortex relate to local feature dissimilarity and physical distance, respectively. We manipulated these factors in a texture segregation experiment wherein human observers identified the orientation of a figure defined by reduced contrast heterogeneity compared to the background. Human performance followed TWCO predictions both qualitatively and quantitatively, as formalized in a computational model. Moreover, we found that when enriched with a Hebbian learning rule, our model also predicted human learning effects: Increases in model gamma synchrony due to perceptual learning predicted improvements in texture segregation across sessions. Taken together, our data suggest that the stimulus-dependence of gamma synchrony captures local image statistics and is linked to the stimulus-dependence of texture segregation, and that the effect of visual experience on gamma synchrony provides a viable perceptual learning mechanism for training-induced improvements in texture segregation. Our results suggest that gamma synchrony with its inherent stimulus dependencies can provide a plausible mechanistic basis for perceptual grouping and visual scene segmentation.

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 16 matches between paragraphs and lines of code.

ccnmaastricht/TextureStimuli-FigureGround

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 1e6889b3cd89e1d7bea784ea41e2b91a64db49b9, 2 December 2024
Languages: MATLAB (7)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (6 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
9 files
At the source: github.com/ccnmaastricht/TextureStimuli-FigureGround

ccnmaastricht/NeuralSynchrony-FigureGround

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 127ff3a044577a35c2bf5bf30530daac1d190963, 9 April 2026
Languages: Python (21), Jupyter (9)
Size: 53 files, 30 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (Dockerfile, requirements.txt), 9 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (26 files), Matplotlib (9 files), pandas (8 files), SciPy (8 files), ArviZ (6 files), Bambi (4 files), seaborn (4 files), statsmodels (2 files), scikit-learn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
32 files
At the source: github.com/ccnmaastricht/NeuralSynchrony-FigureGround

Code availability

The code for data acquisition can be accessed at https://github.com/ccnmaastricht/TextureStimuli-FigureGround, copy archived at ccnmaastricht, 2024. The code for performing all analyses and simulations can be accessed at https://github.com/ccnmaastricht/NeuralSynchrony-FigureGround, copy archived at ccnmaastricht, 2026.

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.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 37 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

Datasets cited

Data availability

All data generated or analyzed during this study are openly accessible at https://doi.org/10.5281/zenodo.10817187.

The following dataset was generated:

KarimianM RobertsMJ De WeerdP SendenM 2024Human Psychophysics Dataset on Figure Ground Segregation in Texture StimuliZenodo10.5281/zenodo.10817187

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

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 5 MeSH terms, 108 references.

Cite

This paper

Karimian, M., Roberts, M. J., De Weerd, P., & Senden, M. (2026). Principles of gamma synchrony predict figure-ground perception in texture stimuli. eLife, 14, RP105482. https://doi.org/10.7554/elife.105482

BibTeX

@article{karimian2026principles,
author = {Karimian, Maryam and Roberts, Mark Jonathan and De Weerd, Peter and Senden, Mario},
title = {{Principles of gamma synchrony predict figure-ground perception in texture stimuli}},
journal = {eLife},
year = {2026},
month = apr,
volume = {14},
pages = {RP105482},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.105482},
url = {https://doi.org/10.7554/elife.105482},
pmid = {41961073},
pmcid = {PMC13068434}
}

RIS

TY - JOUR
AU - Karimian, Maryam
AU - Roberts, Mark Jonathan
AU - De Weerd, Peter
AU - Senden, Mario
TI - Principles of gamma synchrony predict figure-ground perception in texture stimuli
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/04/10
VL - 14
SP - RP105482
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.105482
UR - https://doi.org/10.7554/elife.105482
LA - en
ER -

CSL-JSON

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