Two distinct modes of forebrain circuit dynamics underlie temporal patterning in the vocalizations of young songbirds
Open accessAbstract
Accurate timing is a critical aspect of motor control, yet the temporal structure of many mature behaviors emerges during learning from highly variable exploratory actions. How does a developing brain acquire the precise control of timing in behavioral sequences? To investigate the development of timing, we analyzed the songs of young juvenile zebra finches. These highly variable vocalizations, akin to human babbling, gradually develop into temporally stereotyped adult songs. We find that the durations of syllables and silences in juvenile singing are formed by a mixture of two distinct modes of timing: a random mode producing broadly distributed durations early in development, and a stereotyped mode underlying the gradual emergence of stereotyped durations. Using lesions, inactivations, and localized brain cooling, we investigated the roles of neural dynamics within two premotor cortical areas in the production of these temporal modes. We find that LMAN (lateral magnocellular nucleus of the nidopallium) is required specifically for the generation of the random mode of timing and that mild cooling of LMAN causes an increase in the durations produced by this mode. On the contrary, HVC (used as a proper name) is required specifically for producing the stereotyped mode of timing, and its cooling causes a slowing of all stereotyped components. These results show that two neural pathways contribute to the timing of juvenile songs and suggest an interesting organization in the forebrain, whereby different brain areas are specialized for the production of distinct forms of neural dynamics.
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Birdsong learning is driven by reinforcement learning using a specialized cost function that relies on comparison with a memorized version of a tutor's song, and it involves specialized structures for controlling song variability during learning.
“Likewise, it is believed that birdsong learning is driven by reinforcement learning using a specialized cost function that relies on comparison with a memorized version of a tutor's song (Fiete et al., 2007), and also that it involves specialized structures for controlling song variability during learning (Aronov et al., 2011).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Aronov et al., 2011
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“To remove waste heat generated by current flow through the device (Long and Fee, 2008; Aronov and Fee, 2011), a convective coppermesh heat sink was attached to the hot plate of the device.”
MethodInfluentialTowards quantification of vocal imitation in the zebra finch
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“Because sequences of protosyllables and protogaps developmentally transform into adult song motifs (Tchernichovski et al., 2001; Tchernichovski and Mitra, 2002), these results suggest that the biophysical processes within HVC controlling the timing of early song elements may be related to those controlling the timing…”
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BackgroundNew methods for localizing and manipulating neuronal dynamics in behaving animals
Fee, M. S., & Long, M. A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.010 · Cited by 10 · cited 2× · see citing works
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BackgroundMethodThermal modeling in cylindrical coordinates using effective conductivity
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Research works printed but not linked: 5
- Abeles M 1991 Corticonics Cambridge University Press
- Doya K Sejnowski T Tesauro G Tourezky DS Leen TK 1995 A novel reinforcement model of birdsong vocalization learning Advances in Neural Information Processing Systems Cambridge, MA MIT Press 101 108
- Immelmann K Hinde RA 1969 Song development in the zebra finch and other estrildid finches Bird vocalizations Cambridge University Press 61 74
- Lashley KS Jeffress LA 1951 The problem of serial order in behavior Cerebral Mechanisms in Behavior Wiley 112 136
- Wilson HR 1999 Spikes, decisions, and actions Oxford University Press
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Sorted by citations
Brain heating induced by near-infrared lasers during multiphoton microscopy
Podgorski, K., & Ranganathan, G. N. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00275.2016 · Cited by 273 · see citing works
A hypothesis for basal ganglia-dependent reinforcement learning in the songbird
Fee, M. S., & Goldberg, J. H. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.09.069 · Cited by 231 · see citing works
“More recently, it has been found that localized cooling f LMAN in very young birds slows down the timescales of ubsong babbling, suggesting that neuronal or circuit dyamics within LMAN may play a direct role in the generaion of vocal variability (Aronov et al., 2011).”
BackgroundDynamic sensory cues shape song structure in Drosophila
Coen, P., Clemens, J., Weinstein, A. J., Pacheco, D. A., Deng, Y., & Murthy, M. (2014) · Nature · doi:10.1038/nature13131 · Cited by 166 · see citing works
Yıldırım, M., 杉原, 弘., So, P. T. C., & Sur, M. (2019) · Nature Communications · doi:10.1038/s41467-018-08179-6 · Cited by 164 · see citing works
Growth and splitting of neural sequences in songbird vocal development
Okubo, T. S., Mackevicius, E. L., Payne, H. L., Lynch, G. F., & Fee, M. S. (2015) · Nature · doi:10.1038/nature15741 · Cited by 127 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
“…birdsong learning is driven by reinforcement learning using a specialized cost function that relies on comparison with a memorized version of a tutor’s song (Fiete et al., 2007), and also that it involves specialized structures for controlling song variability during learning (Aronov et al., 2011).”
BackgroundA cortical motor nucleus drives the basal ganglia-recipient thalamus in singing birds
Goldberg, J. H., & Fee, M. S. (2012) · Nature Neuroscience · doi:10.1038/nn.3047 · Cited by 114 · see citing works
Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex
Lynch, G. F., Okubo, T. S., Hanuschkin, A., Hahnloser, R. H. R., & Fee, M. S. (2016) · Neuron · doi:10.1016/j.neuron.2016.04.021 · Cited by 102 · see citing works
“Spectral Analysis of Song Amplitude and Spike Trains Song amplitude was calculated as previously described (Aronov et al., 2011).”
MethodSystematic errors in connectivity inferred from activity in strongly recurrent networks
Das, A., & Fiete, I. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0699-2 · Cited by 98 · see citing works
The role of efference copy in striatal learning
Fee, M. S. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.012 · Cited by 87 · see citing works
Striatal dopamine modulates song spectral but not temporal features through D1 receptors
Leblois, A., & Perkel, D. J. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08095.x · Cited by 79 · see citing works
“The discrepancy of our results with the previous study of Cooper & Goller (2006) may be due to the fact that gaps and syllables do not simply reflect inspiration and expiration, respectively (Aronov et al., 2011).”
ResultTchernichovski, O., & Marcus, G. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.06.002 · Cited by 67 · see citing works
“It is now possible to track trajectories of vocal changes leading to imitation continuously over development [6 ], and to identify the emergence of syllable types and the differentiation of prototype syllables [6 ,7].”
Songbirds work around computational complexity by learning song vocabulary independently of sequence
Lipkind, D., Zai, A. T., Hanuschkin, A., Marcus, G., Tchernichovski, O., & Hahnloser, R. H. R. (2017) · Nature Communications · doi:10.1038/s41467-017-01436-0 · Cited by 59 · see citing works
Actor-critic reinforcement learning in the songbird
Chen, R., & Goldberg, J. H. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.08.005 · Cited by 56 · see citing works
Development of temporal structure in zebra finch song
Glaze, C. M., & Troyer, T. W. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00578.2012 · Cited by 47 · see citing works
Lewandowski, B. C., Vyssotski, A. L., Hahnloser, R. H. R., & Schmidt, M. F. (2013) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2013.04.001 · Cited by 47 · see citing works
“, 1994), localized cooling (Long and Fee, 2008; Aronov et al., 2011), and single cell recordings (Hahnloser et al.”
A canonical neural mechanism for behavioral variability
Darshan, R., Wood, W. E., Peters, S. S., Leblois, A., & Hansel, D. (2017) · Nature Communications · doi:10.1038/ncomms15415 · Cited by 41 · see citing works
Fee, M. S. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00038 · Cited by 40 · see citing works
“Furthermore, local brain cooling within LMAN results in slowing of the characteristic timescales of vocal babbling, suggesting that the biophysical and circuit dynamics within LMAN are involved in generating vocal variability (Aronov et al., 2011).”
BackgroundA rhythm landscape approach to the developmental dynamics of birdsong
Sasahara, K., Tchernichovski, O., Takahasi, M., Suzuki, K., & Okanoya, K. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0802 · Cited by 39 · see citing works
“Several studies of the zebra finch song have reported the differentiation of syllable types [7] and broad unimodal distributions of syllables and silent gaps during early development [12].”
BackgroundDanish, H. H., Aronov, D., & Fee, M. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169568 · Cited by 39 · see citing works
“Syllables and gaps were segmented based on the analysis described by Aronov et al., 2011.”
BackgroundMethodResultInfluentialPrintable microscale interfaces for long-term peripheral nerve mapping and precision control
Otchy, T. M., Michas, C., Lee, B., Gopalan, K., Nerurkar, V., Gleick, J., Semu, D., Darkwa, L., Holinski, B. J., Chew, D. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18032-4 · Cited by 35 · see citing works
“Hence in the fictive singing paradigm, the muscles of the syrinx are articulated by multi-channel stimulation of the nXIIts while the respiratory system is pressurized to levels typical of normal singing (Aronov et al., 2011) by a cannula placed in the abdominal airsac (Brown and Pilny, 2006) (Figure 5d).”
BackgroundDopaminergic modulation of basal ganglia output through coupled excitation–inhibition
Budzillo, A., Duffy, A., Miller, K. E., Fairhall, A. L., & Perkel, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611146114 · Cited by 35 · see citing works
“Area X can exert a strong, precisely timed influence on LMAN via DLM (50, 51, 60, 61).”
BackgroundInfluentialGenerating variability from motor primitives during infant locomotor development
Hinnekens, E., Barbu‐Roth, M. A., Do, M., Berret, B., & Teulier, C. (2023) · eLife · doi:10.7554/elife.87463 · Cited by 28 · see citing works
Wada, K., Hayase, S., Imai, R., Mori, C., Kobayashi, M., Liu, W., Takahasi, M., & Okanoya, K. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12258 · Cited by 28 · see citing works
“This idea is consistent with a report that basal ganglia–thalamocortical circuits, including Area X, are required for the generation of the random mode producing broadly distributed durations of syllables and inter-syllables (Aronov et al., 2011).”
BackgroundResultVocal motor changes beyond the sensitive period for song plasticity
James, L. S., & Sakata, J. T. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00217.2014 · Cited by 28 · see citing works
“For example, because intersyllable gaps coincide with short inspirations during song (Aronov et al. 2011; reviewed in Goller and Cooper 2004; Veit et al. 2011), the shortening of gaps could indicate that birds are more efficient at replenishing their air supply with shorter inspirations.”
BackgroundDittrich, F., Ramenda, C., Grillitsch, D., Frankl‐Vilches, C., Ko, M., Hertel, M., Goymann, W., Maat, A. T., & Gahr, M. L. (2014) · BMC Neuroscience · doi:10.1186/s12868-014-0128-0 · Cited by 27 · see citing works
“The HVC is involved in the con-trol of stereotyped song timing [7,8] and processing of syntactic information [9].”
Building a state space for song learning
Mackevicius, E. L., & Fee, M. S. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.12.001 · Cited by 28 · see citing works
Resurgent Na+ currents promote ultrafast spiking in projection neurons that drive fine motor control
Zemel, B. M., Nevue, A. A., Dagostin, A., Lovell, P. V., Mello, C. V., & von Gersdorff, H. (2021) · Nature Communications · doi:10.1038/s41467-021-26521-3 · Cited by 25 · see citing works
Forebrain circuits underlying the social modulation of vocal communication signals
Matheson, L., Sun, H., & Sakata, J. T. (2016) · Developmental Neurobiology · doi:10.1002/dneu.22298 · Cited by 21 · see citing works
“…and timing (Vu et al., 1994; Yu and Margoliash, 1996; Schmidt, 2003; Ashmore et al., 2005; Thompson and Johnson, 2007; Long and Fee, 2008; Sakata and Brainard, 2008; Wang et al., 2008; reviewed in Jin, 2009; Aronov et al., 2011; Fujimoto et al., 2011; Rajan and Doupe, 2013; Basista et al., 2014).”
BackgroundInfluentialPlasticity of stereotyped birdsong driven by chronic manipulation of cortical-basal ganglia activity
Moorman, S., Ahn, J., & Kao, M. H. (2021) · Current Biology · doi:10.1016/j.cub.2021.04.030 · Cited by 17 · see citing works
“For comparison, the light gray bar indicates the range of proto-syllable durations and the light gray circle indicates the mean duration of proto-syllables in juvenile zebra finches.(32)”
Zhao, Z., Teoh, H. K., Carpenter, J. K., Nemon, F., Kardon, B., Cohen, I., & Goldberg, J. H. (2023) · Current Biology · doi:10.1016/j.cub.2023.11.014 · Cited by 16 · see citing works
Achiro, J. M., Shen, J., & Bottjer, S. W. (2017) · eLife · doi:10.7554/elife.26973 · Cited by 16 · see citing works
“As birds progress to the plastic song phase, they produce more regular syllable types which begin to appear as peaks in the distribution of syllable durations, and therefore are no longer well-fit by an exponential (Aronov et al., 2011; Tchernichovski et al., 2004).”
InfluentialLesions in a songbird vocal circuit increase variability in song syntax
Koparkar, A., Warren, T. L., Charlesworth, J. D., Shin, S., Brainard, M. S., & Veit, L. (2024) · eLife · doi:10.7554/elife.93272 · Cited by 15 · see citing works
The Development of Structured Vocalizations in Songbirds and Humans: A Comparative Analysis
Lipkind, D., Geambașu, A., & Levelt, C. C. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12414 · Cited by 15 · see citing works
Vocal learning in songbirds requires cholinergic signaling in a motor cortex-like nucleus
Puzerey, P. A., Maher, K., Prasad, N., & Goldberg, J. H. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00078.2018 · Cited by 13 · see citing works
“However, the syllables of blocked birds exhibited duration distributions with few peaks and shapes that more closely resembled the distributions of juvenile birds (Fig. 4B; see Fig. 2, B and G, for juvenile distributions; Aronov et al. 2011; Tchernichovski et al. 2001).”
BackgroundMethodInfluentialLong-Distance Retinoid Signaling in the Zebra Finch Brain
Roeske, T. C., Scharff, C., Olson, C. R., Nshdejan, A., & Mello, C. V. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111722 · Cited by 12 · see citing works
Elliott, K. C., Wu, W., Bertram, R., & Johnson, F. (2013) · Developmental Neurobiology · doi:10.1002/dneu.22151 · Cited by 9 · see citing works
“Protosyl- lables are the first vocal sounds driven primarily by the posterior pathway (Aronov et al., 2008, 2011) and our results suggest that protosyllables remain in a largely undifferentiated state when LMAN ablation occurs before their production or differentiation.”
BackgroundZebra Finches in Biomedical Research
Patterson, M. M., & Fee, M. S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-409527-4.00023-7 · Cited by 9 · see citing works
Nicotinic acetylcholine receptors in a songbird brain
Asogwa, N. C., Toji, N., He, Z., Shao, C., Shibata, Y., Tatsumoto, S., Ishikawa, H., Go, Y., & Wada, K. (2022) · The Journal of Comparative Neurology · doi:10.1002/cne.25314 · Cited by 9 · see citing works
Orthogonal topography in the parallel input architecture of songbird HVC
Elliott, K. C., Wu, W., Bertram, R., Hyson, R. L., & Johnson, F. (2017) · The Journal of Comparative Neurology · doi:10.1002/cne.24189 · Cited by 9 · see citing works
Roy, A., & Ben‐Yakar, A. (2023) · Biomedical Optics Express · doi:10.1364/boe.507517 · Cited by 8 · see citing works
Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuitsPreprint
Das, A., & Fiete, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/512053 · Cited by 8 · see citing works
A deep learning approach for the analysis of birdsongPreprint
Koch, T. M., Marks, E. S., & Roberts, T. F. (2024) · eLife · doi:10.7554/elife.101111 · Cited by 8 · see citing works
Let's talk about sex: Mechanisms of neural sexual differentiation in Bilateria
Roggenbuck, E. C., Hall, E. A., Hanson, I. B., Roby, A. A., Zhang, K. K., Alkatib, K. A., Carter, J. A., Clewner, J. E., Gelfius, A. L., Gong, S., et al. (2024) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1636 · Cited by 6 · see citing works
Origins of basal ganglia output signals in singing juvenile birds
Pidoux, M., Bollu, T., Riccelli, T. E., & Goldberg, J. H. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00635.2014 · Cited by 5 · see citing works
Network dynamics underlie learning and performance of birdsong
Bertram, R., Hyson, R. L., Brunick, A. J., Flores, D., & Johnson, F. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.04.004 · Cited by 5 · see citing works
Interactive rhythms in the wild, in the brain, and in silico.
Anichini, M., Kloots, M. D. H., & Ravignani, A. (2020) · Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · doi:10.1037/cep0000224 · Cited by 4 · see citing works
“In several vertebrates, precisely timed sequences of behaviour are not innate but are acquired by juvenile animals (Aronov, Veit, Goldberg, & Fee, 2011).”
BackgroundComparative approaches to the neurobiology of avian vocal learning
Zhao, Z.-L., & Goldberg, J. H. (2025) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2025.102993 · Cited by 2 · see citing works
Songbird mesostriatal dopamine pathways are spatially segregated before the onset of vocal learning
Ramarao, M., Roeser, A. C., Jones, C. D., & Goldberg, J. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285652 · Cited by 2 · see citing works
“Over the next 50 days, the song gradually becomes more stereotyped and similar to the tutor song, finally crystalizing by 90-100 dph [25, 26].”
BackgroundWhat the canary can tell us about singing and the brain
Negro, C. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15350-1.00003-4 · Cited by 2 · see citing works
Bistere, L., Wilczek, S., & Vallentin, D. (2024) · BMC Neuroscience · doi:10.1186/s12868-024-00915-7 · Cited by 2 · see citing works
“In juve-niles, HVC lesions result in the abolition of adult like gap and syllable durations and HVC cooling in adults results in an elongation of syllables and gaps [13].”
Zebra finch tutees not only share the melody but also the rhythm of their tutor’s song
Burchardt, L. S., Varkevisser, J. M., & Spierings, M. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-22811-8 · Cited by 2 · see citing works
“As shown previously, the temporal structure of zebra finch songs becomes increasingly stereotyped during the sensory-motor phase of development 37,38 .”
Learning continually with representational drift
van der Veldt, S., van de Ven, G. M., Moorman, S., & Etter, G. (2026) · Trends Open · doi:10.1016/j.treopn.2026.04.013 · Cited by 1 · see citing works
Lesions in a songbird vocal circuit increase variability in song syntax
Koparkar, A., Warren, T. L., Charlesworth, J. D., Shin, S., Brainard, M. S., & Veit, L. (2024) · eLife · doi:10.7554/elife.93272.3 · Cited by 1 · see citing works
Lesions in a songbird vocal circuit increase variability in song syntaxPreprint
Koparkar, A., Warren, T. L., Charlesworth, J. D., Shin, S., Brainard, M. S., & Veit, L. (2024) · eLife · doi:10.7554/elife.93272.2 · Cited by 1 · see citing works
AVN: A Deep Learning Approach for the Analysis of BirdsongPreprint
Koch, T. M., Marks, E. S., & Roberts, T. F. (2024) · eLife · doi:10.7554/elife.101111.1 · Cited by 1 · see citing works
Differential Regulation of Androgen Receptor and DNA Methylation in Songbirds
Wada, K. (2016) · Epigenetics and human health · doi:10.1007/978-3-319-24493-8_10
Complex Dynamics Enabled by Basic Neural Features
Regel, D. (2020) · doi:10.53846/goediss-8033
Lesions in a songbird vocal circuit increase variability in song syntaxPreprint
Koparkar, A., Warren, T. L., Charlesworth, J. D., Shin, S., Brainard, M. S., & Veit, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.25.550508
Lesions in a songbird vocal circuit increase variability in song syntaxPreprint
Koparkar, A., Warren, T. L., Charlesworth, J. D., Shin, S., Brainard, M. S., & Veit, L. (2024) · eLife · doi:10.7554/elife.93272.1
AVN: A Deep Learning Approach for the Analysis of BirdsongPreprint
Koch, T. M., Marks, E. S., & Roberts, T. F. (2025) · eLife · doi:10.7554/elife.101111.2
Temperature robustness of the timing network within songbird premotor nucleus HVC
Khare, A., Sederman, D., & Jin, D. Z. (2026) · Journal of Computational Neuroscience · doi:10.1007/s10827-026-00922-z
Dual pathway architecture in songbirds enables robust sensorimotor learningPreprint
Sankar, R., Suryawanshi, A., Rougier, N. P., & Leblois, A. (2026) · eLife · doi:10.64898/2026.05.07.723469 · Cited by 2 · see citing works
- Authors
- Aronov, D., Veit, L., Goldberg, J. H., & Fee, M. S.
- Affiliations
- McGovern Institute for Brain Research (US)
- Corresponding author
- Fee, Michale S.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-11-09
- Volume
- 31
- Issue
- 45
- Pages
- 16353-16368
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3241969
- OpenAlex
- W2058495600
- Cited by
- 64
- References
- 64
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 5 · 2022: 1 · 2021: 2 · 2020: 6 · 2019: 3 · 2018: 1 · 2017: 7 · 2016: 5 · 2015: 4 · 2014: 7 · 2013: 3 · 2012: 4
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Hertz Foundation
- National Institutes of Health
- National Institute on Deafness and Other Communication Disorders (R01DC009183)
- National Institute of Neurological Disorders and Stroke (K99NS067062)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- zebra finch, syllable duration, HVC, premotor cortex, neural dynamics, brain cooling, song learning
- MeSH terms
- Animals, Behavior, Animal, Computer Simulation, Male, Models, Neurological, Nerve Net, Neural Pathways, Respiration, Sound Spectrography, Spectrum Analysis, Stereotyped Behavior, Time Factors, Time Perception, Vocalization, Animal, Prosencephalon, Nonlinear Dynamics, Songbirds
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