Synchronous Activity in the Auditory Forebrain Represents Temporal Landmarks in Vocal Communication Signals.

Code ↔ Paper

9 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 9 matches · 4 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted

[1] § Materials and Methods › Statistical analysis ↔ Figure 4/Code/Figure4_C_LFPvsMUAe_Envelope.R, the whole file · a weak match · score 0.66 · lmerTest, emmeans, lme4, pairwise, recording depth, intercepts · Report a mismatch
[2] § Materials and Methods › Statistical analysis ↔ Figure 4/Code/Figure4_C_LFPvsMUAe_Event.R, the whole file · a weak match · score 0.66 · lmerTest, emmeans, lme4, pairwise, recording depth, intercepts · Report a mismatch
[3] § Materials and Methods › Single-unit analyses ↔ matlab/gcmi_cc.m, the whole file · a weak match · score 0.63 · Gaussian copula, lower bound, mutual information, MI · Report a mismatch
[4] § Materials and Methods › Single-unit analyses ↔ matlab/gcmi_model_cd.m, the whole file · a weak match · score 0.63 · Gaussian copula, lower bound, mutual information, MI · Report a mismatch
[5] § Materials and Methods › Statistical analysis ↔ Supplementary Figures/Code/Figure_S10_C_Landmarks_Stat.R, lines 50 to 137 · score 0.63 · linear mixed, frequency bands, theta, beta, gamma, ITPCzs · Report a mismatch
[6] § Materials and Methods › Statistical analysis ↔ Supplementary Figures/Code/Figure_S10_C_Event_Stat.R, lines 50 to 137 · score 0.62 · linear mixed, frequency bands, theta, beta, gamma, ITPCzs · Report a mismatch
[7] § Materials and Methods › Neural network latent space ↔ Supplementary Figures/Code/Figure_S13.m, lines 14 to 48 · score 0.61 · LFP features, t-SNE, perplexity, BiLSTM, space, layer · Report a mismatch
[8] § Results › Functional anatomy of temporal decoding ↔ Figure 4/Code/Figure4_AB_3_4.m, lines 593 to 626 · score 0.55 · 0.5–1.5 mm, PeakEnv, CR, ML, DV, channels · Report a mismatch
[9] § Materials and Methods › Neural data recording ↔ pyoperant/behavior/three_ac_matching.py, lines 7 to 120 · score 0.51 · Gentner lab, Python, motifs, wave, stimuli · Report a mismatch

Paper

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The authors' code

R · 83 lines · 3.2 KB · no license · 1 match

# Envelope ---- Compare Performance LFP vs MUAe
# Load necessary libraries
library(tidyverse)
library(lme4)
library(officer)
library(emmeans)
# Set options to adjust limit for large datasets
emm_options(pbkrtest.limit = 7320, lmerTest.limit = 7320)
# Define significance threshold
significance_level <- 0.05
# Section 1: Define paths and load data
Load_Path <- 'C:/Amir/Phd research/Article Phd/Thesis/Ahmadi et al 1/Data Figures/New/'
Save_Path <- 'C:/Amir/Phd research/Article Phd/Thesis/Ahmadi et al 1/Statistical Analysis/Results/'
# Set working directory and load data
setwd(Load_Path)
fileIn <- 'Fig4_C_Envelope_Session.csv'
SaveName <- 'Envelope_Depths_LFPvsMUAe.csv'
df3A.Envelope <- read.csv(fileIn)
# Section 2: Data Preprocessing
# Convert relevant columns to factors
df3A.Envelope <- within(df3A.Envelope, {
Birds_Name <- factor(Name)
Sex_Birds <- factor(Sex)
Depth <- factor(Depth)
Session <- factor(Session_Number)
})
# Summarize the data
summary(df3A.Envelope)
# Initialize Word document
doc <- read_docx()
doc <- body_add_par(doc, paste0('File: ', gsub('\\.csv$', '', SaveName)), style = "Normal")
# Section 3: Model Fitting and Analysis for Decoding Performance Comparison (LFP vs MUAe)
# Reshape data to long format for comparison of LFP and MUAe
df3A.Envelope.long <- pivot_longer(df3A.Envelope, cols = c("Perf_LFP", "Perf_MUAe"), names_to = 'Signal', values_to = 'Performance')
# Fit null and effect models for performance comparison
null_model <- lmer(Performance ~ Depth + (1 | Session), data = df3A.Envelope.long, REML = FALSE)
effect_model <- lmer(Performance ~ Signal * Depth + (1 | Session), data = df3A.Envelope.long, REML = FALSE)
# Perform ANOVA to compare models
anova_result <- anova(null_model, effect_model)
write.csv(anova_result, file = paste0(Save_Path, 'Stat_', SaveName, '_LFP_MUAe_anova_result.csv'))
# Extract and format ANOVA and effect size results
chi_square <- anova_result[2, "Chisq"]
df <- anova_result[2, "Df"]
p_value <- anova_result[2, "Pr(>Chisq)"]
delta_r <- fixef(effect_model)["(Intercept)"]
se <- sqrt(vcov(effect_model)["(Intercept)", "(Intercept)"])
formatted_result <- sprintf('χ^2 (%d) = %.2f, p = %.8f; Δr = %.2f ± %.3f SE',
df, chi_square, p_value, delta_r, se)
doc <- body_add_par(doc, paste0("**Decoding Performance Comparison (LFP vs MUAe): ", formatted_result, "**"), style = "Normal")
# Conduct post hoc analysis only if ANOVA is significant
if (p_value < significance_level) {
posthoc <- emmeans(effect_model, ~ Signal | Depth)
posthoc_contrast <- as.data.frame(contrast(posthoc, method = "pairwise"))
significant_contrast <- posthoc_contrast[posthoc_contrast$p.value < significance_level, ]
write.csv(significant_contrast, file = paste0(Save_Path, 'Stat_', SaveName, '_LFP_MUAe_posthoc_analysis.csv'))
}
# Section 4: Save Results in Word Format
print(doc, target = paste0(Save_Path, 'Stat_', SaveName, '_results.docx'))
# Visualization of Decoding Performance by Depth
library(ggplot2)
emmip(posthoc, ~ Depth | Signal) +
theme_minimal() +
labs(title = "Decoding Performance by Signal Type and Recording Depth",
y = "Estimated Performance",
x = "Depth")

Figure4_C_LFPvsMUAe_Envelope.R at commit 1309c01, no license · at the source

Overview

Authors: Amirmasoud Ahmadi^1,2, Hermina Robotka¹, Manfred Gahr¹, Frederic Theunissen³

ORCID iDs: Amirmasoud Ahmadi, Hermina Robotka, Frederic Theunissen

Institutions: Max Planck Institute for Biological Intelligence (Germany); Ludwig-Maximilians-Universität München (Germany); University of California, Berkeley (United States)

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 43, article e2108252026

Dates: received 15 November 2025; accepted 24 September 2026; published online 28 October 2026; in print 28 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1523/jneurosci.2108-25.2026 · PMID 42810968 · PMCID PMC13717546 · OpenAlex W7214797781

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: other (organism), systems (subfield)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Single-unit activity, calcium imaging, Physiology & signal measures

Keywords: animal communication, auditory cortex, auditory objects, neural decoding, speech entrainment, zebra finches

MeSH: Auditory Cortex*, Auditory Perception*, Prosencephalon*, Vocalization, Animal*, Acoustic Stimulation, Animals, Female, Finches, Male (* major topic)

Journal subjects: Systems/Circuits

Topic: Animal Vocal Communication and Behavior (Ecology, Evolution, Behavior and Systematics, Agricultural and Biological Sciences), according to OpenAlex

Funding: HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) (R01DC-016783, R01DC-018321); Bundesministerium für Forschung, Technologie und Raumfahrt (BMBF) (01GQ1701); Alexander von Humboldt-Stiftung

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

Abstract

Precise temporal segmentation of sound is critical for identifying and interpreting complex acoustic signals such as human speech (Pisoni and Luce, 1987) and bird song (Gentner, 2008). Using the zebra finch model system, we investigated the neural coding of the songs' temporal features. We recorded neural activity from avian primary and secondary auditory cortex-like brain areas using electrode arrays in awake birds (four males and three females) exposed to conspecific songs. By applying a deep-learning approach with bidirectional long short-term memory networks (Daly, 2023), we demonstrate that not only the amplitude envelope of song stimuli but also discrete, time-locked envelope landmarks can be decoded from multichannel neuronal responses to songs. The times of envelope peak amplitude and peak slope were decoded with average accuracies of 40% and maximum accuracies of 70% at 20 ms timescales. The decoding of these landmarks enabled the segmentation of song into syllables with maximum accuracy of 90% at 10 ms timescales. Decoding accuracy for temporal events was greater in the primary auditory than in the secondary auditory area, suggesting a serial processing of the segmentation and identification operation. These findings reveal that neural entrainment to salient temporal events in vocal communication signals is a ubiquitous, specialized feature of the vertebrate primary auditory forebrain (Mesgarani et al., 2009; Heelan et al., 2019) providing a mechanism for segmenting continuous acoustic streams into behaviorally relevant units for higher-order processing.

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 9 matches between paragraphs and lines of code.

gentnerlab/pyoperant

License: BSD-3-Clause

State: the link answers, verified on 30 October 2026

Evidence: files inventoried

Commit: f1f2fe67658d71bfd698c6c8403554d124494d34, 9 October 2026

Languages: Python (55), Jupyter (5), C (1)

Size: 111 files, 61 scripts

Software Heritage: archived

Found in: the text, “Neural data recording”

Holds: README, license file, environment (setup.py, docs/requirements.txt), tests, documentation, 4 notebooks

Not found: CITATION.cff, continuous integration

Tools: NumPy (17 files), SciPy (4 files), Matplotlib (1 file)

Availability: 1 check, the latest on 30 October 2026: the link answers

30 October 2026: the link answers

62 files

docs/conf.py, Python, 306 lines
notebooks/.ipynb_checkpoints/pyoperant-checkpoint.ipynb, Jupyter, 114 lines
notebooks/comedi.ipynb, Jupyter, 1 line
notebooks/pyaudio.ipynb, Jupyter, 1 line
notebooks/pydaqmx.ipynb, Jupyter, 1 line
notebooks/pyoperant.ipynb, Jupyter, 125 lines
pyoperant/__init__.py, Python, 1 line
pyoperant/analysis.py, Python, 119 lines
pyoperant/behavior/__init__.py, Python, 4 lines
pyoperant/behavior/base.py, Python, 474 lines
pyoperant/behavior/lights.py, Python, 706 lines
pyoperant/behavior/place_pref_24hr.py, Python, 558 lines
pyoperant/behavior/shape.py, Python, 615 lines
pyoperant/behavior/three_ac_matching.py, Python, 169 lines, 1 match
pyoperant/behavior/two_alt_choice.py, Python, 585 lines
pyoperant/behavior/two_alt_choice_early_responses.py, Python, 642 lines
pyoperant/components.py, Python, 606 lines
pyoperant/errors.py, Python, 37 lines
pyoperant/hwio.py, Python, 251 lines
pyoperant/interfaces/__init__.py, Python, 1 line
pyoperant/interfaces/arduino_.py, Python, 231 lines
pyoperant/interfaces/base_.py, Python, 15 lines
pyoperant/interfaces/comedi_.py, Python, 75 lines
pyoperant/interfaces/console_.py, Python, 18 lines
pyoperant/interfaces/pyaudio_.py, Python, 187 lines
pyoperant/interfaces/pydaqmx_.py, Python, 1 line
pyoperant/interfaces/raspi_gpio_.py, Python, 291 lines
pyoperant/interfaces/spike2_.py, Python, 27 lines
pyoperant/local.py, Python, 38 lines
pyoperant/local_pi.py, Python, 140 lines
pyoperant/local_pi_revc.py, Python, 148 lines
pyoperant/local_pi_revd.py, Python, 209 lines
pyoperant/local_vogel.py, Python, 147 lines
pyoperant/local_zog.py, Python, 241 lines
pyoperant/panels.py, Python, 198 lines
pyoperant/queues.py, Python, 364 lines
pyoperant/reinf.py, Python, 140 lines
pyoperant/song_recording/__init__.py, Python, 22 lines
pyoperant/song_recording/_pcm.py, Python, 63 lines
pyoperant/song_recording/calibration.py, Python, 288 lines
pyoperant/song_recording/classifier.py, Python, 270 lines
pyoperant/song_recording/cnn_numpy.py, Python, 198 lines
pyoperant/song_recording/features.py, Python, 360 lines
pyoperant/song_recording/gate.py, Python, 497 lines
pyoperant/song_recording/melspec.py, Python, 177 lines
pyoperant/song_recording/monitor.py, Python, 1,907 lines
pyoperant/song_recording/noise_model.py, Python, 632 lines
pyoperant/song_recording/smoother.py, Python, 438 lines
pyoperant/song_recording/variability.py, Python, 223 lines
pyoperant/utils.py, Python, 507 lines
scripts/sync_mic_cal.py, Python, 149 lines
scripts/test_ir.py, Python, 80 lines
scripts/test_panel.py, Python, 105 lines
scripts/tune_servo.py, Python, 286 lines
setup.py, Python, 40 lines
src/comedi_poll.c, C, 83 lines
tests/fixtures.py, Python, 194 lines
tests/test_components.py, Python, 358 lines
tests/test_imports.py, Python, 94 lines
tests/test_instantiate.py, Python, 720 lines
tests/test_tune_servo.py, Python, 159 lines
LICENSE.txt, License, 12 lines

At the source: github.com/gentnerlab/pyoperant

robince/gcmi

License: GPL-3.0

State: the link answers, verified on 30 October 2026

Evidence: files inventoried

Commit: cd5426bb4c6b7094858f37c3710a4ae9367f8ef1, 23 July 2026

Languages: MATLAB (45), Python (10), C++ (10), C (1)

Size: 90 files, 66 scripts

Software Heritage: archived

Found in: the text, “Single-unit analyses”

Holds: README, license file, environment (python/pyproject.toml, python/uv.lock), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: NumPy (5 files), Numba (3 files), EEGLAB (2 files), SciPy (2 files), Statistics and Machine Learning Toolbox (1 file)

Availability: 2 checks, the latest on 30 October 2026: the link answers

30 October 2026: the link answers
30 September 2026: the link answers

68 files

benchmarks/cpp_mex_default_function_map.m, MATLAB, 10 lines
benchmarks/run_matlab_benchmarks.m, MATLAB, 798 lines
benchmarks/run_python_benchmarks.py, Python, 392 lines
buildfile.m, MATLAB, 45 lines
matlab/Contents.m, MATLAB, 36 lines
matlab/bias_cmi_ggg_bits.m, MATLAB, 23 lines
matlab/bias_ent_g_bits.m, MATLAB, 23 lines
matlab/bias_mi_gd_bits.m, MATLAB, 42 lines
matlab/bias_mi_gg_bits.m, MATLAB, 14 lines
matlab/cmi_ggg.m, MATLAB, 93 lines
matlab/cmi_ggg_vec.m, MATLAB, 118 lines
matlab/copnorm.m, MATLAB, 10 lines
matlab/cpp_mex/mex/copnorm_slice_cpp.cpp, C++, 25 lines
matlab/cpp_mex/mex/gcmi_cpp_blas_probe.cpp, C++, 32 lines
matlab/cpp_mex/mex/gcmi_cpp_omp_probe.cpp, C++, 21 lines
matlab/cpp_mex/mex/gcmi_cpp_ping.cpp, C++, 16 lines
matlab/cpp_mex/mex/gcmi_cpp_runtime_probe.cpp, C++, 34 lines
matlab/cpp_mex/mex/info_cc_slice_cpp.cpp, C++, 33 lines
matlab/cpp_mex/mex/info_cc_slice_cpp_capi.c, C, 96 lines
matlab/cpp_mex/mex/info_cd_slice_cpp.cpp, C++, 29 lines
matlab/cpp_mex/src/copnorm_kernel.cpp, C++, 105 lines
matlab/cpp_mex/src/gcmi_kernels.cpp, C++, 371 lines
matlab/cpp_mex/src/info_cc_slice_bridge.cpp, C++, 36 lines
matlab/cpp_mex/tests/run_gcmi_cpp_mex_smoke_tests.m, MATLAB, 3 lines
matlab/cpp_mex/tooling/gcmi_cpp_mex_clean.m, MATLAB, 9 lines
matlab/cpp_mex/tooling/gcmi_cpp_mex_compile.m, MATLAB, 189 lines
matlab/cpp_mex/tooling/gcmi_cpp_mex_config.m, MATLAB, 154 lines
matlab/cpp_mex/tooling/gcmi_cpp_mex_package.m, MATLAB, 17 lines
matlab/cpp_mex/tooling/gcmi_cpp_mex_test.m, MATLAB, 156 lines
matlab/ctransform.m, MATLAB, 8 lines
matlab/ent_g.m, MATLAB, 42 lines
matlab/gccmi_ccc.m, MATLAB, 56 lines
matlab/gccmi_ccd.m, MATLAB, 73 lines
matlab/gcmi_cc.m, MATLAB, 46 lines, 1 match
matlab/gcmi_mixture_cd.m, MATLAB, 66 lines
matlab/gcmi_model_cd.m, MATLAB, 46 lines, 1 match
matlab/gcmi_version.m, MATLAB, 4 lines
matlab/maxstar.m, MATLAB, 42 lines
matlab/mi_gg.m, MATLAB, 71 lines
matlab/mi_gg_vec.m, MATLAB, 129 lines
matlab/mi_mixture_gd.m, MATLAB, 108 lines
matlab/mi_mixture_gd_vec.m, MATLAB, 164 lines
matlab/mi_model_gd.m, MATLAB, 82 lines
matlab/mi_model_gd_vec.m, MATLAB, 156 lines
matlab/tests/Contents.m, MATLAB, 3 lines
matlab/tests/run_gcmi_regression_tests.m, MATLAB, 255 lines
matlab/validate_discrete_labels.m, MATLAB, 35 lines
matlab/vecchol.m, MATLAB, 74 lines
matlab_examples/bias_demo.m, MATLAB, 37 lines
matlab_examples/block_delay.m, MATLAB, 12 lines
matlab_examples/block_index.m, MATLAB, 6 lines
matlab_examples/continuous_meg.m, MATLAB, 229 lines
matlab_examples/cpp_continuous_native.m, MATLAB, 150 lines
matlab_examples/cpp_discrete_native.m, MATLAB, 155 lines
matlab_examples/discrete_eeg.m, MATLAB, 122 lines
matlab_examples/eeg_temporal_interaction.m, MATLAB, 269 lines
python/gcmi.py, Python, 33 lines
python/src/gcmi/__init__.py, Python, 5 lines
python/src/gcmi/_core.py, Python, 926 lines
python/src/gcmi/_dispatch.py, Python, 95 lines
python/src/gcmi/_numba.py, Python, 557 lines
python/src/gcmi/_version.py, Python, 41 lines
python/tests/conftest.py, Python, 9 lines
python/tests/test_batch_kernels.py, Python, 192 lines
python/tests/test_gcmi.py, Python, 304 lines
setup_gcmi.m, MATLAB, 78 lines
LICENSE, License, 231 lines
README.md, Text, 272 lines

At the source: github.com/robince/gcmi

amirmasoud92/ZF_Neural_Decoding

License: none: the authors keep all their rights

State: the link answers, verified on 30 October 2026

Evidence: files inventoried

Commit: 1309c015a622304e6ef253a4efe6787a4d6ec248, 20 July 2025

Languages: MATLAB (34), R (32)

Size: 309 files, 66 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README

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

Tools: lme4 (32 files), emmeans (11 files), ggplot2 (11 files), Statistics and Machine Learning Toolbox (8 files), tidyverse (7 files), easystats (3 files), lmerTest (3 files), Signal Processing Toolbox (3 files), reshape2 (3 files), Violinplot-Matlab (2 files)

Availability: 1 check, the latest on 30 October 2026: the link answers

30 October 2026: the link answers

67 files

Additional Functions/Freq_Imp_Calculation_Network.m, MATLAB, 84 lines
Additional Functions/MUAe_Extraction.m, MATLAB, 31 lines
Additional Functions/randomPhaseShuffle.m, MATLAB, 12 lines
Figure 2/Code/Figure_2_B.m, MATLAB, 209 lines
Figure 2/Code/setupProject.m, MATLAB, 62 lines
Figure 2/Utils/setupProject.m, MATLAB, 62 lines
Figure 3/Code/Figure3_A_Envelope.R, R, 135 lines
Figure 3/Code/Figure3_A_Event.R, R, 88 lines
Figure 3/Code/Figure3_A_EventDetection.R, R, 88 lines
Figure 3/Code/Figure3_A_Landmarks.R, R, 126 lines
Figure 3/Code/Figure3_B_LFP_vs_MUAe_Envelope.R, R, 124 lines
Figure 3/Code/Figure3_B_LFP_vs_MUAe_Event.R, R, 123 lines
Figure 3/Code/Figure3_B_LFP_vs_MUAe_Landmarks.R, R, 123 lines
Figure 3/Code/Figure3_B_Real_vs_Shuffle_Envelope.R, R, 124 lines
Figure 3/Code/Figure3_B_Real_vs_Shuffle_Event.R, R, 132 lines
Figure 3/Code/Figure3_B_Real_vs_Shuffle_PeakEnv.R, R, 123 lines
Figure 3/Code/Figure3_B_Real_vs_Shuffle_PeakRate.R, R, 124 lines
Figure 3/Code/Figure_3_A.m, MATLAB, 39 lines
Figure 3/Code/Figure_3_B.m, MATLAB, 125 lines
Figure 3/Code/setupProject.m, MATLAB, 62 lines
Figure 3/Utils/Violinplot-Matlab-master/Violin.m, MATLAB, 707 lines
Figure 3/Utils/Violinplot-Matlab-master/plotdetails.m, MATLAB, 8 lines
Figure 3/Utils/Violinplot-Matlab-master/violinplot.m, MATLAB, 189 lines
Figure 3/Utils/create_lfp_muae_scatter_plot.m, MATLAB, 201 lines
Figure 4/Code/Figure4_AB_1.m, MATLAB, 343 lines
Figure 4/Code/Figure4_AB_2.m, MATLAB, 345 lines
Figure 4/Code/Figure4_AB_3_4.m, MATLAB, 680 lines, 1 match
Figure 4/Code/Figure4_C_Envelope.R, R, 108 lines
Figure 4/Code/Figure4_C_Event.R, R, 113 lines
Figure 4/Code/Figure4_C_LFPvsMUAe_Envelope.R, R, 83 lines, 1 match
Figure 4/Code/Figure4_C_LFPvsMUAe_Event.R, R, 86 lines, 1 match
Figure 4/Code/Figure4_C_LFPvsMUAe_PeakEnv.R, R, 83 lines
Figure 4/Code/Figure4_C_LFPvsMUAe_PeakRate.R, R, 83 lines
Figure 4/Code/Figure4_C_PeakEnv.R, R, 106 lines
Figure 4/Code/Figure4_C_PeakRate.R, R, 106 lines
Figure 4/Code/setupProject.m, MATLAB, 62 lines
Figure 4/Utils/othercolor/othercolor.m, MATLAB, 155 lines
Figure 5/Code/Figure5_B.m, MATLAB, 215 lines
Figure 5/Code/Figure_5_C.m, MATLAB, 539 lines
Figure 5/Code/setupProject.m, MATLAB, 62 lines
Figure 5/Utils/othercolor.m, MATLAB, 155 lines
Figure 6/Code/Figure6_A.m, MATLAB, 46 lines
Figure 6/Utils/setupProject.m, MATLAB, 62 lines
Figure 7/Code/Figure7_Paper.m, MATLAB, 114 lines
Figure 7/Code/setupProject.m, MATLAB, 62 lines
Figure 7/Utils/plot_3D_activity_Amir.m, MATLAB, 141 lines
Supplementary Figures/Code/FigS12_Parts.m, MATLAB, 252 lines
Supplementary Figures/Code/Figure6S_B_Envelope.R, R, 98 lines
Supplementary Figures/Code/Figure6S_B_EventDetection.R, R, 98 lines
Supplementary Figures/Code/Figure6S_B_Landmark.R, R, 98 lines
Supplementary Figures/Code/FigureS7_Envelope.R, R, 98 lines
Supplementary Figures/Code/FigureS7_Event.R, R, 98 lines
Supplementary Figures/Code/FigureS7_Landmark.R, R, 98 lines
Supplementary Figures/Code/FigureS8_Envelope.R, R, 84 lines
Supplementary Figures/Code/FigureS8_Event.R, R, 84 lines
Supplementary Figures/Code/FigureS8_PeakEnv.R, R, 85 lines
Supplementary Figures/Code/FigureS8_PeakRate.R, R, 85 lines
Supplementary Figures/Code/Figure_S10_A.m, MATLAB, 109 lines
Supplementary Figures/Code/Figure_S10_B.m, MATLAB, 29 lines
Supplementary Figures/Code/Figure_S10_C_Envelope_Stat.R, R, 146 lines
Supplementary Figures/Code/Figure_S10_C_Event_Stat.R, R, 137 lines, 1 match
Supplementary Figures/Code/Figure_S10_C_Landmarks_Stat.R, R, 137 lines, 1 match
Supplementary Figures/Code/Figure_S13.m, MATLAB, 92 lines, 1 match
Supplementary Figures/Code/Figure_S9.m, MATLAB, 139 lines
Supplementary Figures/Code/setupProject.m, MATLAB, 62 lines
Supplementary Figures/Utils/FR_Amir_MUA_S10.m, MATLAB, 74 lines
README.md, Text, 101 lines

At the source: github.com/amirmasoud92/ZF_Neural_Decoding

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
193 scripts, each with its path and the digest of its content;
9 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

All primary electrophysiological recordings (RHD format), the corresponding playback stimuli, and backpack-microphone tracks have been deposited in the Edmond repository under https://doi.org/10.17617/3.UINR2V. Playback metadata are included in the same record as comma-separated values (.csv) files. The source data supporting every figure, the complete decoding outputs, and the single-unit response matrices for all playback stimuli are available in Edmond under https://doi.org/10.17617/3.AUMDAU. All analysis scripts and figure reproduction code are openly available on GitHub at https://github.com/amirmasoud92/ZF_Neural_Decoding.

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, 30 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 9 MeSH terms, 3 funders, 57 references.

Cite

This paper

Ahmadi, A., Robotka, H., Gahr, M., & Theunissen, F. (2026). Synchronous Activity in the Auditory Forebrain Represents Temporal Landmarks in Vocal Communication Signals. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(43), e2108252026. https://doi.org/10.1523/jneurosci.2108-25.2026

BibTeX

@article{ahmadi2026synchronous, author = {Ahmadi, Amirmasoud and Robotka, Hermina and Gahr, Manfred and Theunissen, Frederic}, title = {{Synchronous Activity in the Auditory Forebrain Represents Temporal Landmarks in Vocal Communication Signals}}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, year = {2026}, month = oct, volume = {46}, number = {43}, pages = {e2108252026}, publisher = {Society for Neuroscience}, issn = {0270-6474}, doi = {10.1523/jneurosci.2108-25.2026}, url = {https://doi.org/10.1523/jneurosci.2108-25.2026}, pmid = {42810968}, pmcid = {PMC13717546} }

RIS

TY - JOUR AU - Ahmadi, Amirmasoud AU - Robotka, Hermina AU - Gahr, Manfred AU - Theunissen, Frederic TI - Synchronous Activity in the Auditory Forebrain Represents Temporal Landmarks in Vocal Communication Signals T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience J2 - J Neurosci PY - 2026 DA - 2026/10/28 VL - 46 IS - 43 SP - e2108252026 SN - 0270-6474 PB - Society for Neuroscience DO - 10.1523/jneurosci.2108-25.2026 UR - https://doi.org/10.1523/jneurosci.2108-25.2026 LA - en ER -

CSL-JSON

{ "id": "10.1523/jneurosci.2108-25.2026", "type": "article-journal", "title": "Synchronous Activity in the Auditory Forebrain Represents Temporal Landmarks in Vocal Communication Signals", "container-title": "The Journal of neuroscience : the official journal of the Society for Neuroscience", "author": [ { "family": "Ahmadi", "given": "Amirmasoud" }, { "family": "Robotka", "given": "Hermina" }, { "family": "Gahr", "given": "Manfred" }, { "family": "Theunissen", "given": "Frederic" } ], "container-title-short": "J Neurosci", "volume": "46", "issue": "43", "page": "e2108252026", "DOI": "10.1523/jneurosci.2108-25.2026", "PMID": "42810968", "PMCID": "PMC13717546", "ISSN": "0270-6474", "publisher": "Society for Neuroscience", "URL": "https://doi.org/10.1523/jneurosci.2108-25.2026", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 28 ] ] } }

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