Article | Published:

Growth and splitting of neural sequences in songbird vocal development

Nature volume 528, pages 352357 (17 December 2015) | Download Citation

Abstract

Neural sequences are a fundamental feature of brain dynamics underlying diverse behaviours, but the mechanisms by which they develop during learning remain unknown. Songbirds learn vocalizations composed of syllables; in adult birds, each syllable is produced by a different sequence of action potential bursts in the premotor cortical area HVC. Here we carried out recordings of large populations of HVC neurons in singing juvenile birds throughout learning to examine the emergence of neural sequences. Early in vocal development, HVC neurons begin producing rhythmic bursts, temporally locked to a ‘prototype’ syllable. Different neurons are active at different latencies relative to syllable onset to form a continuous sequence. Through development, as new syllables emerge from the prototype syllable, initially highly overlapping burst sequences become increasingly distinct. We propose a mechanistic model in which multiple neural sequences can emerge from the growth and splitting of a common precursor sequence.

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References

  1. 1.

    & Hippocampal theta sequences reflect current goals. Nature Neurosci. 18, 289–294 (2015)

  2. 2.

    & Hippocampal place-cell sequences depict future paths to remembered goals. Nature 497, 74–79 (2013)

  3. 3.

    & Preplay of future place cell sequences by hippocampal cellular assemblies. Nature 469, 397–401 (2011)

  4. 4.

    , & Hippocampal replay of extended experience. Neuron 63, 497–507 (2009)

  5. 5.

    , , & Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nature Neurosci. 11, 823–833 (2008)

  6. 6.

    , , & Internally generated cell assembly sequences in the rat hippocampus. Science 321, 1322–1327 (2008)

  7. 7.

    Time cells in the hippocampus: a new dimension for mapping memories. Nature Rev. Neurosci. 15, 732–744 (2014)

  8. 8.

    , & Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012)

  9. 9.

    , , & Neural antecedents of self-initiated actions in secondary motor cortex. Nature Neurosci. 17, 1574–1582 (2014)

  10. 10.

    , & Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014)

  11. 11.

    Sequential organization of multiple movements: involvement of cortical motor areas. Annu. Rev. Neurosci. 24, 631–651 (2001)

  12. 12.

    Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68, 362–385 (2010)

  13. 13.

    , & Neural network dynamics. Annu. Rev. Neurosci. 28, 357–376 (2005)

  14. 14.

    in Bird Vocalizations (ed. ) 61–74 (Cambridge Univ. Press, 1969)

  15. 15.

    & Birdsong and human speech: common themes and mechanisms. Annu. Rev. Neurosci. 22, 567–631 (1999)

  16. 16.

    Neural mechanisms for learned birdsong. Learn. Mem. 16, 655–669 (2009)

  17. 17.

    Birdsong: from behavior to neuron. Annu. Rev. Neurosci. 8, 125–170 (1985)

  18. 18.

    & Translating birdsong: songbirds as a model for basic and applied medical research. Annu. Rev. Neurosci. 36, 489–517 (2013)

  19. 19.

    , & An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419, 65–70 (2002)

  20. 20.

    & Singing-related activity of identified HVC neurons in the zebra finch. J. Neurophysiol. 97, 4271–4283 (2007)

  21. 21.

    , & Support for a synaptic chain model of neuronal sequence generation. Nature 468, 394–399 (2010)

  22. 22.

    , , & Elemental gesture dynamics are encoded by song premotor cortical neurons. Nature 495, 59–64 (2013)

  23. 23.

    , & Neural coding of syntactic structure in learned vocalizations in the songbird. J. Neurosci. 31, 10023–10033 (2011)

  24. 24.

    , , & Precise auditory-vocal mirroring in neurons for learned vocal communication. Nature 451, 305–310 (2008)

  25. 25.

    , & Central control of song in the canary, Serinus canarius. J. Comp. Neurol. 165, 457–486 (1976)

  26. 26.

    & Using temperature to analyse temporal dynamics in the songbird motor pathway. Nature 456, 189–194 (2008)

  27. 27.

    , & A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320, 630–634 (2008)

  28. 28.

    & Brain pathways for learned and unlearned vocalizations differ in zebra finches. J. Neurosci. 10, 1541–1556 (1990)

  29. 29.

    et al. The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong. Neuron 80, 494–506 (2013)

  30. 30.

    & Motor origin of precise synaptic inputs onto forebrain neurons driving a skilled behavior. J. Neurosci. 35, 299–307 (2015)

  31. 31.

    The Zebra Finch: A Synthesis of Field and Laboratory Studies (Oxford Univ. Press, 1996)

  32. 32.

    , & Juvenile zebra finches can use multiple strategies to learn the same song. Proc. Natl Acad. Sci. USA 101, 18177–18182 (2004)

  33. 33.

    , , & Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564–2569 (2001)

  34. 34.

    , , & Two distinct modes of forebrain circuit dynamics underlie temporal patterning in the vocalizations of young songbirds. J. Neurosci. 31, 16353–16368 (2011)

  35. 35.

    , & Learning to breathe and sing: development of respiratory-vocal coordination in young songbirds. J. Neurophysiol. 106, 1747–1765 (2011)

  36. 36.

    & Towards quantification of vocal imitation in the zebra finch. J. Comp. Physiol. A 188, 867–878 (2002)

  37. 37.

    & Development of temporal structure in zebra finch song. J. Neurophysiol. 109, 1025–1035 (2013)

  38. 38.

    & A technique for characterizing the development of rhythms in bird song. PLoS One 3, e1461 (2008)

  39. 39.

    et al. Stepwise acquisition of vocal combinatorial capacity in songbirds and human infants. Nature 498, 104–108 (2013)

  40. 40.

    & Quantification of developmental birdsong learning from the subsyllabic scale to cultural evolution. Proc. Natl Acad. Sci. USA 108 (Suppl. 3), 15572–15579 (2011)

  41. 41.

    , & Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC. J. Comput. Neurosci. 23, 283–299 (2007)

  42. 42.

    & Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC. Phys. Rev. E 74, 011918 (2006)

  43. 43.

    & Development of neural circuitry for precise temporal sequences through spontaneous activity, axon remodeling, and synaptic plasticity. PLoS One 2, e723 (2007)

  44. 44.

    , , & Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron 65, 563–576 (2010)

  45. 45.

    A learning rule for the emergence of stable dynamics and timing in recurrent networks. J. Neurophysiol. 94, 2275–2283 (2005)

  46. 46.

    , & Inhibition and recurrent excitation in a computational model of sparse bursting in song nucleus HVC. J. Neurophysiol. 102, 1748–1762 (2009)

  47. 47.

    , , , & Two neural streams, one voice: pathways for theme and variation in the songbird brain. Neuroscience 277, 806–817 (2014)

  48. 48.

    , & Interplay of inhibition and excitation shapes a premotor neural sequence. J. Neurosci. 35, 1217–1227 (2015)

  49. 49.

    & Peripheral motor dynamics of song production in the zebra finch. Ann. NY Acad. Sci. 1016, 130–152 (2004)

  50. 50.

    Evolution by Gene Duplication (Springer-Verlag, 1970)

  51. 51.

    , , , & A procedure for an automated measurement of song similarity. Anim. Behav. 59, 1167–1176 (2000)

  52. 52.

    , , , & Studying the song development process: rationale and methods. Ann. NY Acad. Sci. 1016, 348–363 (2004)

  53. 53.

    & Novel motor gestures for phonation during inspiration enhance the acoustic complexity of birdsong. Proc. R. Soc. Lond. B 268, 2301–2305 (2001)

  54. 54.

    & Behavioral and neural signatures of readiness to initiate a learned motor sequence. Curr. Biol. 23, 87–93 (2013)

  55. 55.

    & An automated procedure for evaluating song imitation. PLoS One 9, e96484 (2014)

  56. 56.

    & Miniature motorized microdrive and commutator system for chronic neural recording in small animals. J. Neurosci. Methods 112, 83–94 (2001)

  57. 57.

    , & In vivo recording of single-unit activity during singing in zebra finches. Cold Spring Harb. Protoc. 2014, 1273–1283 (2014)

  58. 58.

    , & Neural mechanisms of vocal sequence generation in the songbird. Ann. NY Acad. Sci. 1016, 153–170 (2004)

  59. 59.

    , & Sleep-related neural activity in a premotor and a basal-ganglia pathway of the songbird. J. Neurophysiol. 96, 794–812 (2006)

  60. 60.

    & A cortical motor nucleus drives the basal ganglia-recipient thalamus in singing birds. Nature Neurosci. 15, 620–627 (2012)

  61. 61.

    Spikes: Exploring the Neural Code (MIT Press, 1997)

  62. 62.

    & Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput. 13, 717–749 (2001)

  63. 63.

    , , , & Chronux: a platform for analyzing neural signals. J. Neurosci. Methods 192, 146–151 (2010)

  64. 64.

    & Observed Brain Dynamics (Oxford Univ. Press, 2008)

  65. 65.

    & From frequency to quefrency: a history of the Cepstrum. IEEE Signal Process. Mag. 21, 95–106 (2004)

  66. 66.

    , & A neural circuit mechanism for regulating vocal variability during song learning in zebra finches. eLife 3, e03697 (2014)

  67. 67.

    & Ensemble coding of vocal control in birdsong. J. Neurosci. 25, 652–661 (2005)

  68. 68.

    , & Brainstem and forebrain contributions to the generation of learned motor behaviors for song. J. Neurosci. 25, 8543–8554 (2005)

  69. 69.

    , & Sparse contour representations of sound. IEEE Signal Process. Lett. 19, 684–687 (2012)

  70. 70.

    , , & Long-range order in canary song. PLOS Comput. Biol. 9, e1003052 (2013)

  71. 71.

    , & Pattern Classification 2nd edn (Wiley, 2001)

  72. 72.

    100 Statistical Tests 3rd edn (Sage Publications, 2006)

  73. 73.

    Handbook of Biological Statistics 3rd edn (Sparky House Publishing, 2014)

  74. 74.

    & Functional significance of long-term potentiation for sequence learning and prediction. Cereb. Cortex 6, 406–416 (1996)

  75. 75.

    & Spike timing-dependent plasticity: from synapse to perception. Physiol. Rev. 86, 1033–1048 (2006)

  76. 76.

    & A hypothesis for basal ganglia-dependent reinforcement learning in the songbird. Neuroscience 198, 152–170 (2011)

  77. 77.

    , , & Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong. J. Neurophysiol. 92, 2274–2282 (2004)

  78. 78.

    , , & Learning the microstructure of successful behavior. Nature Neurosci. 14, 373–380 (2011)

  79. 79.

    , , & Vocal exploration is locally regulated during song learning. J. Neurosci. 32, 3422–3432 (2012)

  80. 80.

    , & The zebra finch paradox: song is little changed, but number of neurons doubles. J. Neurosci. 32, 761–774 (2012)

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Acknowledgements

We thank M. Wilson, J. Kornfeld, M. Jazayeri, S. Seung, N. Ji, and M. Stetner for comments on the manuscript. Funding to M.S.F. was provided by the NIH (grant no. R01DC009183) and by the Mathers Foundation, to T.S.O. by the Nakajima Foundation and Schoemaker Fellowship, to E.L.M. by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program, and to H.L.P. by the National Science Foundation (NSF) Graduate Research Fellowship Program (no. DGE-114747) and the NSF Integrative Graduate Education and Research Traineeship (no. 0801700). The modelling work was begun in the Methods in Computational Neuroscience course at the Marine Biological Laboratory (NIH grant number R25MH062204).

Author information

Affiliations

  1. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Tatsuo S. Okubo
    • , Emily L. Mackevicius
    • , Galen F. Lynch
    •  & Michale S. Fee
  2. Department of Neurobiology, Stanford University, Stanford, California 94305, USA

    • Hannah L. Payne

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Contributions

The study was conceived and designed by T.S.O. and M.S.F. Experimental data were collected by T.S.O. Data were analysed by T.S.O and M.S.F. with contributions from G.F.L. The modelling study was performed by E.L.M. and H.L.P. in collaboration with T.S.O. and M.S.F. All five authors contributed to writing the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Michale S. Fee.

Extended data

Supplementary information

Text files

  1. 1.

    Supplementary Data

    This file contains the Script to reproduce Figure 5 a-d and Extended Data Figure 10 a-d.

Videos

  1. 1.

    Model: alternating differentiation

    Video showing network progression over development for the alternating differentiation shown in Fig. 5a-d. Network diagrams are plotted, and text indicates the developmental stage (subsong, protosyllable stage, or splitting stage), and iteration number.

  2. 2.

    Model: bout-onset differentiation

    Video showing network progression over development for the bout-onset differentiation shown in Extended Data Fig. 10a-d. Network diagrams are plotted, and text indicates the developmental stage (subsong, protosyllable stage, or splitting stage), and iteration number.

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DOI

https://doi.org/10.1038/nature15741

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