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Social familiarity strengthens neural and vocal responses to conspecific calls in zebra finches.

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 · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Classification analysis ↔ famtools/fam_analysis/classification_accuracy_train_test_split.py, the whole file · a weak match · score 0.65 · min max, classifier accuracies, stratification, split, training, fit
  2. [2] § Materials and methods › Classification analysis ↔ famtools/fam_analysis/classification_accuracy.py, the whole file · a weak match · score 0.63 · min max, classifier accuracies, stratification, fit, split, training
  3. [3] § Results › Specificity of vocal replies ↔ famtools/fam_analysis/classification_accuracy.py, the whole file · a weak match · score 0.59 · random forest, classification accuracy, Confusion matrix, setup, error, probabilities
  4. [4] § Results › Specificity of vocal replies ↔ famtools/fam_analysis/classification_accuracy_train_test_split.py, the whole file · a weak match · score 0.56 · random forest, classification accuracy, Confusion matrix, error, probabilities, max
  5. [5] § Materials and methods › Data analysis › Electrophysiological data analysis. ↔ famtools/fam_analysis/permutationt_euc_dist.py, the whole file · a weak match · score 0.56 · Euclidean Distance, shuffled distribution, PCA, trajectories, baseline
  6. [6] § Results › Neural activity in vocal premotor nucleus HVC is differentially modulated by caller familiarity ↔ famtools/fam_plot/lines_plot_zscores_sig.py, lines 171–254 · score 0.51 · confidence interval, standard error, PSTH, histogram, Firing rate, score
  7. [7] § Results › Neural activity in vocal premotor nucleus HVC is differentially modulated by caller familiarity ↔ famtools/fam_plot/lines_plot_zscores_sig.py, lines 171–254 · score 0.51 · confidence interval, standard error, PSTH, histogram, Firing rate, score

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 92 lines · 3.9 KB · no license · 2 matches

  1. def classification_accuracy_train_test_split(csf_df, rtimes=1000, type='rforest', csf_keys=['max_resp', 'resp_dur', 'precision_score'], input_labels=None, pred='test', test_size=0.1, normalize=False):
  3. if isinstance(csf_df, pd.DataFrame):
  4. # Drop rows with nan values, and print how many are there
  5. if csf_df.isna().any().any():
  6. print('Nan values found, corresponding rows were eliminated. Nan Counts:')
  7. print(csf_df.isna().sum())
  8. csf_df = csf_df.dropna()
  10. # Define labels
  11. if input_labels is None:
  12. labels = csf_df['name']
  13. label_encoder = LabelEncoder()
  14. input_labels = label_encoder.fit_transform(labels)
  15. else:
  16. labels = input_labels
  18. # Define features
  19. input_features = csf_df[csf_keys]
  20. else:
  21. input_features = csf_df
  24. # Set up classifier dictionary
  25. classifiers = {'rforest': RandomForestClassifier(random_state=42),
  26. 'sv': SVC(C=1, probability=True, random_state=42),
  27. 'logr': LogisticRegression(solver='lbfgs', max_iter=1000, random_state=42),
  28. 'knn': KNeighborsClassifier(n_neighbors=6)}
  30. classifier = classifiers.get(type)
  31. if classifier is None:
  32. raise ValueError("Invalid model_type specified. Choose from 'rforest', 'sv', 'logr', 'knn'.")
  34. accuracy_scores = []
  35. auc_scores = []
  36. recall_scores = []
  37. specificity_scores = []
  38. conf_matrix_sum = np.zeros((2, 2))
  40. for run in range(rtimes):
  41. inner_seed = np.random.randint(0, 1_000_000)
  42. # Reinitialize the classifier with a new random state for each run
  43. classifier = classifiers[type]
  44. classifier.random_state = inner_seed
  46. # Split data into train and test sets
  47. ft_train, ft_test, lb_train, lb_test = train_test_split(
  48. input_features, input_labels, test_size=test_size, random_state=inner_seed, stratify=input_labels)
  50. if normalize: # Works for df
  51. # Normalize datasets
  52. #scaler = MinMaxScaler()
  53. scaler = StandardScaler()
  54. scaled_train = scaler.fit_transform(ft_train)
  55. scaled_test = scaler.transform(ft_test)
  56. #ft_train = ft_train.apply(lambda x: zscore(x))
  57. #ft_test = ft_test.apply(lambda x: zscore(x))
  58. else: # for pcs
  59. scaled_train = ft_train
  60. scaled_test = ft_test
  62. # Train and predict
  63. classifier.fit(scaled_train, lb_train)
  64. if pred == 'test':
  65. predictions = classifier.predict(scaled_test)
  66. accuracy = accuracy_score(lb_test, predictions)
  67. curr_conf_matrix = confusion_matrix(lb_test, predictions)
  69. # Recall and Specificity
  70. tn, fp, fn, tp = confusion_matrix(lb_test, predictions).ravel()
  71. recall = recall_score(lb_test, predictions, pos_label=1)
  72. specificity = tn / (tn + fp)
  74. elif pred == 'train':
  75. predictions = classifier.predict(scaled_train)
  76. accuracy = accuracy_score(lb_train, predictions)
  77. curr_conf_matrix = confusion_matrix(lb_train, predictions)
  79. accuracy_scores.append(accuracy)
  80. recall_scores.append(recall)
  81. specificity_scores.append(specificity)
  82. conf_matrix_sum += curr_conf_matrix
  84. # Calculate ROC curve and AUC
  85. predictions_proba = classifier.predict_proba(scaled_test)[:, 1] # Probability of the positive class
  86. fpr, tpr, _ = roc_curve(lb_test, predictions_proba) # True labels vs predicted probabilities
  87. auc_scores = auc(fpr, tpr) # Calculate AUC
  89. mean_conf_matrix = conf_matrix_sum / conf_matrix_sum.sum(axis=1, keepdims=True) * 100
  90. mean_accuracy = np.mean(accuracy_scores)
  92. return accuracy_scores, mean_conf_matrix, auc_scores, recall_scores, specificity_scores

classification_accuracy_train_test_split.py at commit 302813b, no license · at the source

Overview

Authors: Carlos M. Gomez-Guzman1, Daniela Vallentin1, Jonathan I. Benichov1
ORCID iDs: Daniela Vallentin, Jonathan I. Benichov
  1. Neural Circuits for Vocal Communication Research Group, Max Planck Institute for Biological Intelligence, Seewiesen, Germany
Institutions: Max Planck Institute for Biological Intelligence
Journal: PLoS computational biology, volume 22, issue 3, article e1014024
Dates: received 15 April 2025; accepted 15 February 2026; published online 11 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014024 · PMID 41811819 · PMCID PMC12978450 · OpenAlex W7135020610
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Single-unit activity, calcium imaging
MeSH: Finches*, Recognition, Psychology*, Social Behavior*, Vocalization, Animal*, Animals, Auditory Perception, Computational Biology, Interneurons, Male, Neurons, Prosencephalon (* major topic)
Journal subjects: Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Interneurons, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Birds, Zoology, Psychology, Behavior, Animal Behavior, Animal Communication, Bird Song, Social Sciences, Ornithology, Animal Sociality, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Animal Models, Zebra Finch, Physiology, Electrophysiology, Membrane Potential, Action Potentials, Neurophysiology
Topic: Animal Vocal Communication and Behavior (Developmental Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (5768: VA 742/7-1 – 532521431, 1315: 327654276, BE 7545/1-1, VA 742/6-1 – 547921981); EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) (ERC-2017-StG - 757459 MIDNIGHT)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Across animals, dyadic vocal interactions often occur within complex acoustic environments containing numerous signalers. The influence of socially relevant acoustic signals on the neural circuits controlling interactive vocal behavior remains poorly understood. We examined this issue in zebra finches, highly social songbirds that maintain nearly continuous vocal contact through the exchange of short innate calls. We developed a behavioral paradigm that elicits differential responses to familiar and unfamiliar vocal partners, enabling the prediction of social context based on individual birds’ response patterns. We then used high-density Neuropixels probes to record neural activity within a vocal premotor nucleus in the songbird forebrain, while birds listened to familiar and unfamiliar contact calls. We found that the activity of putative projection neurons and interneurons in this vocal premotor nucleus was modulated by the familiarity of heard calls, with interneurons exhibiting stronger responses to familiar calls. Furthermore, we found that measures of vocal responsiveness correlated with neural response parameters during listening. Specifically, we observed that increased vocal response rates, rapidity, and temporal consistency for familiar call playbacks were correlated with elevated mean and peak firing rates, as well as prolonged activity, in HVC interneurons. These results demonstrate how socially salient auditory information can affect a forebrain premotor circuit to maintain the specificity of vocal interactions within complex and dynamic social environments.

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.

vallentinlab/SocialContext_calls_HVC

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 302813b411c1b21c70683baf10226caf873730c0, 11 September 2025
Languages: Python (68), Jupyter (4)
Size: 75 files, 72 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), SciPy (5 files), Matplotlib (4 files), seaborn (4 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
73 files
At the source: github.com/vallentinlab/SocialContext_calls_HVC

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;
  • 72 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);
  • 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

Data are available here: https://github.com/vallentinlab/SocialContext_calls_HVC.

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 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 11 MeSH terms, 2 funders, 61 references.

Cite

This paper

Gomez-Guzman, C. M., Vallentin, D., & Benichov, J. I. (2026). Social familiarity strengthens neural and vocal responses to conspecific calls in zebra finches. PLoS computational biology, 22(3), e1014024. https://doi.org/10.1371/journal.pcbi.1014024

BibTeX

@article{gomezguzman2026social,
author = {Gomez-Guzman, Carlos M. and Vallentin, Daniela and Benichov, Jonathan I.},
title = {{Social familiarity strengthens neural and vocal responses to conspecific calls in zebra finches}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1014024},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014024},
url = {https://doi.org/10.1371/journal.pcbi.1014024},
pmid = {41811819},
pmcid = {PMC12978450}
}

RIS

TY - JOUR
AU - Gomez-Guzman, Carlos M.
AU - Vallentin, Daniela
AU - Benichov, Jonathan I.
TI - Social familiarity strengthens neural and vocal responses to conspecific calls in zebra finches
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/11
VL - 22
IS - 3
SP - e1014024
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014024
UR - https://doi.org/10.1371/journal.pcbi.1014024
LA - en
ER -

CSL-JSON

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"family": "Gomez-Guzman",
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"container-title-short": "PLoS Comput Biol",
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