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Convergent vocal representations in parrot and human forebrain motor networks

Nature volume 640, pages 427–434 (2025)Cite this article

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Abstract

Cortical networks for the production of spoken language in humans are organized by phonetic features1,2, such as articulatory parameters3,4 and vocal pitch5,6. Previous research has failed to find an equivalent forebrain representation in other species7,8,9,10,11. To investigate whether this functional organization is unique to humans, here we performed population recordings in the vocal production circuitry of the budgerigar (Melopsittacus undulatus), a small parrot that can generate flexible vocal output12,13,14,15, including mimicked speech sounds16. Using high-density silicon probes17, we measured the song-related activity of a forebrain region, the central nucleus of the anterior arcopallium (AAC), which directly projects to brainstem phonatory motor neurons18,19,20. We found that AAC neurons form a functional vocal motor map that reflects the spectral properties of ongoing vocalizations. We did not observe this organizing principle in the corresponding forebrain circuitry of the zebra finch, a songbird capable of more limited vocal learning21. We further demonstrated that the AAC represents the production of distinct vocal features (for example, harmonic structure and broadband energy). Furthermore, we discovered an orderly representation of vocal pitch at the population level, with single neurons systematically selective for different frequency values. Taken together, we have uncovered a functional representation in a vertebrate brain that displays unprecedented commonalities with speech-related motor cortices in humans. This work therefore establishes the parrot as an important animal model for investigating speech motor control and for developing therapeutic solutions for addressing a range of communication disorders22,23.

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Fig. 1: Comparative vocal behaviour and underlying neural activity.
Fig. 2: Different neural coding schemes in zebra finch RA and budgerigar AAC.
Fig. 3: Distinct mapping of vocal acoustic types in the AAC state space.
Fig. 4: Neural representation of vocal pitch in the AAC.

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Data availability

Data used in this study have been uploaded to Zenodo (https://doi.org/10.5281/zenodo.14057061)75. The Switchboard Telephone Speech Corpus is accessible through the Linguistic Data Consortium (https://catalog.ldc.upenn.edu/LDC97S62) and existing annotations of the corpus are provided by The Institute for Signal and Information Processing (https://isip.piconepress.com/projects/switchboard/). Source data are provided with this paper.

Code availability

The primary code used in this study is available at Zenodo (https://doi.org/10.5281/zenodo.14057061)75.

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Acknowledgements

We thank A. Bahle, C. Elemans, A. Fishbein, D. Jin, C. Kemere, M. Schmidt and current members of the Long laboratory for comments on earlier versions of this manuscript, B. Cooper, T. Movshon and D. Sanes for helpful conversations and A. Paulson for technical assistance. This research was supported by Simons Collaboration on the Global Brain (M.A.L.).

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Authors and Affiliations

  1. NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA

    Zetian Yang & Michael A. Long

  2. Center for Neural Science, New York University, New York, NY, USA

    Zetian Yang & Michael A. Long

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  1. Zetian Yang
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  2. Michael A. Long
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Contributions

Z.Y. and M.A.L conceived the study and designed the experiments; Z.Y. conducted the research; Z.Y. performed data analyses; Z.Y. and M.A.L. created the figures; Z.Y. wrote the initial draft of the manuscript; Z.Y. and M.A.L. edited and reviewed the final manuscript; M.A.L acquired funding and supervised the project.

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Correspondence to Michael A. Long.

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Extended data figures and tables

Extended Data Fig. 1 Acoustic representation of vocal repertoires across species.

a-c, Mean pitch, duration, and entropy variance from a human (a), a zebra finch (b), and a budgerigar (c). Data represent the same spoken words and vocal elements analyzed in Fig. 1a–f. d, Overlay of data from panels a through c.

Extended Data Fig. 2 Histology and single unit quality.

a, Histological validation of silicon probe placement in zebra finch RA. Left, sagittal slice illustrating probe position, marked by DiI applied to the probe shank (red). Right, expanded view of the boxed region highlighting RA (white arrows) on the right. b, Histological validation of silicon probe placement in budgerigar AAC. Left, brightfield image of a coronal slice showing the position of the silicon probe, marked by DiI applied to the probe shank (red). Right, expanded view of the boxed region indicating dorsal AAC (AACd) and ventral AAC (AACv) by white and yellow arrows, respectively. c, Example single-unit waveforms and autocorrelograms from budgerigar AAC recordings. Mean waveforms (red) are overlaid on single-trial waveform traces, which are represented as gray lines. In the autocorrelogram, orange vertical lines indicate the refractory period (−1 to 1 ms). d, e, Distributions of peak-to-peak amplitude (d) and the percentage of refractory period violations (e) across all AAC single units (n = 220 from 4 birds). Illustrations of the zebra finch in a are reproduced from ref. 60, Elsevier.

Extended Data Fig. 3 Vocal motor responses in AAC and RA.

a, Mean firing rates of AAC neurons during vocalization and baseline for each individual budgerigar. Exact p-values displayed within each plot are from two-sided Wilcoxon signed-rank tests (n = 49, 81, 43, 47 neurons for each plot). b, Population-averaged responses aligned to the onset of vocal elements that are preceded by a silent gap of at least 100 ms. c, Population-averaged responses aligned to the offset of vocal elements followed by a silent gap of at least 100 ms. d, Spiking activity from a population of AAC neurons (BG3) during example vocalization, playback, and quiet baseline periods. e,f, Mean firing rates of AAC neurons during vocalization compared with playback (e) and during playback compared with baseline (f). Exact p-values displayed within each plot are from two-sided Wilcoxon signed-rank tests (n = 43, 47 neurons for the BG3 and BG4 plots, respectively). g, Bursting ratios of AAC neurons during vocalization and baseline for each individual budgerigar. h, Distribution of interspike intervals of AAC neurons pooled across budgerigars. i,j, Mean firing rates (i) and bursting ratios (j) of RA neurons during vocalization and baseline for each zebra finch. Illustrations of the zebra finch in i are reproduced from ref. 60, Elsevier.

Extended Data Fig. 4 Neural similarity in budgerigar AAC is not driven by vocal reuse of elemental components.

a, Sonogram of an example zebra finch song recorded using the same piezoelectric microphone as used for budgerigar recordings. b, Schematic of the methodology for quantifying correlations between unique and repeated vocal segments. Each zebra finch syllable was decomposed into 20-ms segments using a sliding window with a 10-ms step size. Correlations for repeated segments (vocal reuse) were calculated between corresponding segments across 10 renditions (2 renditions shown for illustration) of each syllable. Correlations for unique segments (vocal similarity) were calculated between segments from different syllables. c, Distributions of correlation values between vocal similarity (orange) and reuse (blue), with 0.6 set as a threshold to distinguish between the two conditions. d, Distribution of correlation values across all vocal segments analyzed in Fig. 2 for each budgerigar, with percentage of correlations above 0.6 indicated on the right, representing potential vocal reuse. e, Correlation between spectral similarity and neural similarity matrices for an example budgerigar (left) and accompanying population data (right), excluding all cases with spectral correlation values exceeding 0.6. ρ denotes Spearman’s correlation value. P-values displayed within each plot are from two-sided permutation tests (n = 216,848, 1,113,918 pairs of vocal segments for each plot, respectively).

Extended Data Fig. 5 Single-neuron representation of vocal acoustic categories.

a, Distribution of fundamental frequencies in budgerigar vocalizations shows a clear minimum at 700 Hz. b-d, Distribution of the selectivity index for low frequency (b), harmonic (c), and noisy (d) vocalizations across the AAC population. Colored bins indicate neurons with a selectivity larger than 0.5 for each index. e, Venn diagram displaying the number of neurons with selectivity indices larger than 0.5 for each of the three categories.

Extended Data Fig. 6 Constructing the neural state space.

a, Schematic illustrating the construction of the neural state space. Population neural response within 25-ms time windows were mapped to a three-dimensional space using PCA (see Methods). b, Scree plots depicting the eigenvalues and explained variances for the first 10 neural PCs. Each line represents a single budgerigar. c, Harmonic index (indicated by color of each dot) plotted on state space diagram for all 4 budgerigars. Low frequency notes (that is, gray dots) not included in this analysis.

Extended Data Fig. 7 Comparison of vocal repertoires across budgerigars.

a, UMAP projections of latent representations of budgerigar vocalizations. Each plot represents an individual bird. b, Combined UMAP plot displaying data from all budgerigars.

Extended Data Fig. 8 Populational representation of acoustic categories and neural dynamics during vocal production in AAC.

a, Cumulative distribution of Euclidean distances between neural states within (colored line) or across (black line) acoustic categories for each budgerigar. Distance within each category is significantly smaller than between categories (P < 10−100 for all tests). P-values shown within each plot are from two-sided Wilcoxon rank-sum tests (n = 66,349,440, 144,270,591, 671,739,531, 44,391,753, 1,854,597,921 instances of distances [1st plot]; n = 613,278, 7,657,741, 57,829,635, 3,801,903, 101,861,288 instances of distances [2nd plot]; n = 13,512,201, 318,087,253, 270,688,278, 94,772,028, 1,578,197,393 instances of distances [3rd plot]; n = 20,196,190, 86,638,866, 710,136,141, 37,840,650, 1,317,021,524 instances of distances [4th plot]). b, Mean difference between within-category and between-category neural distances derived from the true data (black vertical lines) plotted against the null distribution generated by randomly shifting spike times (see Methods). Exact p-values displayed in each plot are from one-sided permutation tests (n = 1,000 permutations). c, Cumulative distribution of Euclidean distances between neural states within (solid line) or across (dotted line) acoustic categories, based on a subsample of non-overlapping neural states (see Methods). P-values shown within each plot are from two-sided Wilcoxon rank-sum tests (n = 1,435,416 within and 2,902,569 between category instances of distances [1st plot]; n = 119,859 within and 168,561 between category instances of distances [2nd plot]; n = 1,113,510 within and 2,508,576 between category instances of distances [3rd plot]; n = 1,326,700 within and 2,000,210 between category instances of distances [4th plot]). d, Similar to c but with each within-category distances plotted separately as colored lines. P-values shown within each plot are from two-sided Wilcoxon rank-sum tests (n = 106,491, 216,811, 1,038,961, 73,153, 2,902,569 instances of distances [1st plot]; n = 990, 11,628, 101,025, 6,216, 168,561 instances of distances [2nd plot]; n = 21,528, 503,506, 445,096, 143,380, 2,508,576 instances of distances [3rd plot]; n = 30,381, 125,250, 1,113,778, 57,291, 2,000,210 instances of distances [4th plot]). P < 10−100 for all tests. e, Neural trajectories for three example vocal elements spanning two acoustic types. Color indicates time within the element. Grayscale dots are the neural states corresponding to the three acoustic types (see labels in panel at left).

Extended Data Fig. 9 A neural frequency axis in AAC state space.

a, Pitches (white lines on top) were estimated from vocalizations with harmonic indices that exceeded the median of the distribution (shaded region). b, Distribution of estimated pitches for each individual budgerigar. c, Schematic of the calculation of the neural frequency axis. The axis was computed as the vector between the mean neural responses to low and high pitches in half of the data. The other half of the data was then used to calculate the relationship between pitch and projection onto the axis (see Methods). d, Slope of the relationship between pitch and projections onto the neural frequency axis for each budgerigar. Black vertical lines denote slope from data; gray distributions indicate slopes from pitch-shuffled data. Exact p-values displayed in each plot are derived from one-sided permutation tests (n = 5,000 permutations). e, Population neural responses at different time windows relative to vocalizations were mapped to a two-dimensional state space using PCA. Colored dots represent neural states underlying vocalizations with frequency estimated (color indicates pitch), and gray dots are associated with less harmonic vocalizations whose pitch was difficult to estimate. f, Performance of a linear model in predicting pitch values using scores of the first two PCs for neural responses calculated within a motor time window (−30 to −5 ms), compared to two other time windows with large temporal shifts (−125 to −100 ms and 100 to 125 ms). P-values displayed within the plot are from two-sided Wilcoxon rank-sum tests (n = 14,566 neural states) with Bonferroni correction. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, minimum and maximum values excluding outliers. g, Population-level pitch representation (Fig. 4c) displayed separately for calls and warble syllables. Colored dots denote neural states associated with pitches in calls (left) or warble syllables (right). Gray dots represent neural states for other vocalizations.

Extended Data Fig. 10 Pitch encoding in budgerigar AAC.

a, Schematic of pitch tuning index calculation. b, Pitch tuning in three example neurons from Fig. 4g, presented as binned scatter plots (gray) and averaged firing rates (mean ± SEM) across pitches (green) for all vocal elements (left), only calls (middle), and only warble syllables (right). Blue circles represent baseline firing rates. c, Relative contributions of four acoustic features in GLM models explaining single AAC neuron responses shown for all neurons (left), pitch-tuned neurons (pitch tuning index > 6; middle), and non-tuned neurons (pitch tuning index <6; right), respectively. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, minimum and maximum values excluding outliers. Exact p-values shown within each plot are from Bonferroni-corrected, two-sided Wilcoxon signed-rank tests (n = 220, 112, 108 neurons for each plot, respectively). d, Relationship between decoded and measured pitches across all calls for each budgerigar, shown as binned scatter plots. Linear decoders trained on warble syllables were used to predict pitch in calls. R values represent Pearson correlation between decoded and measured pitches.

Extended Data Fig. 11 Neurons in zebra finch RA do not appear to encode pitch within the song.

a-c, Burst-associated pitch values for an example RA neuron. Black boxes in a denote vocalization periods associated with bursting events (indicated by red bars in c), with vocal periods shifted by 12 ms relative to burst events. Pitch contour within each period is highlighted in b, with the mean pitch value marked by a green dot and a number next to the contour. d, Pitch values for all bursting events across the RA population. Each dot represents a burst event and its associated pitch. e, Relationship between firing rates and pitch for three example RA neurons, presented as binned scatter plots (gray) and averaged firing rates (mean ± SEM) across pitches (blue). f, Distributions of the range of burst-associated pitch values for observed data (blue) and for randomly sampled pitch values from d. g, Distributions of the variance of burst-associated pitch values for observed data (blue) and for randomly sampled pitch values from d. In f and g, Exact p-values shown on top are derived from two-sided Wilcoxon rank-sum tests (n = 116 samples).

Extended Data Fig. 12 Pitch decoding using population activity.

a, Relationship between decoded and measured pitches using all RA neurons for each zebra finch shown as binned scatter plots. b, Relationship between decoded and measured pitches using all AAC neurons for each budgerigar. In a and b, R values represent Pearson correlation between decoded and measured pitches.

Extended Data Fig. 13 Behavioral recording methodology.

a, Photograph of recording arena for budgerigars with one budgerigar tethered on each side. b, Example recordings from an omnidirectional ambient microphone (top) and two piezoelectric microphones implanted on two budgerigars (bottom). Piezoelectric microphones enable excellent isolation of individual vocalizations which are mixed in the ambient microphone.

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Various perspectives of the neural state space The neural state space of AAC fully rotated to better display the three-dimensional representation of different acoustic categories. (Modified from Fig. 3f.)

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Yang, Z., Long, M.A. Convergent vocal representations in parrot and human forebrain motor networks. Nature 640, 427–434 (2025). https://doi.org/10.1038/s41586-025-08695-8

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