1. Neuroscience

Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain

  1. James N McGregor  Is a corresponding author
  2. Abigail L Grassler
  3. Paul I Jaffe
  4. Amanda Louise Jacob
  5. Michael S Brainard
  6. Samuel J Sober
  1. Emory University, United States
  2. University of California, San Francisco, United States
https://doi.org/10.7554/eLife.75691
Share this article
Cite this article
  1. James N McGregor
  2. Abigail L Grassler
  3. Paul I Jaffe
  4. Amanda Louise Jacob
  5. Michael S Brainard
  6. Samuel J Sober
(2022)
Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain
eLife 11:e75691.
https://doi.org/10.7554/eLife.75691
  2. Download BibTeX
  3. Download .RIS

Abstract

Songbirds and humans share the ability to adaptively modify their vocalizations based on sensory feedback. Prior studies have focused primarily on the role that auditory feedback plays in shaping vocal output throughout life. In contrast, it is unclear how non-auditory information drives vocal plasticity. Here, we first used a reinforcement learning paradigm to establish that somatosensory feedback (cutaneous electrical stimulation) can drive vocal learning in adult songbirds. We then assessed the role of a songbird basal ganglia thalamocortical pathway critical to auditory vocal learning in this novel form of vocal plasticity. We found that both this circuit and its dopaminergic inputs are necessary for non-auditory vocal learning, demonstrating that this pathway is critical for guiding adaptive vocal changes based on both auditory and somatosensory signals. The ability of this circuit to use both auditory and somatosensory information to guide vocal learning may reflect a general principle for the neural systems that support vocal plasticity across species.

Data availability

Source data are provided for all main figures and relevant figure supplements (Figure 2b-f, Figure 2 - Figure Supplements 1-7, Figure 3b-e, Figure 3 - Figure Supplement 1, and Figure 4b-d). MATLAB code for generating these figures is also provided in the associated source code files. Data and source code have also been uploaded to a public data repository on figshare, in a project titled 'Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain.'

The following data sets were generated

Article and author information

Author details

  1. James N McGregor

    Neuroscience Graduate Program, Emory University, Atlanta, United States
    For correspondence
    jmcgregor2292@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5187-0984

  2. Abigail L Grassler

    Neuroscience Graduate Program, Emory University, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Paul I Jaffe

    Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0680-3923

  4. Amanda Louise Jacob

    Department of Biology, Emory University, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Michael S Brainard

    Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9425-9907

  6. Samuel J Sober

    Department of Biology, Emory University, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1140-7469

Funding

National Institutes of Health (R01- EB022872)

  • James N McGregor
  • Abigail L Grassler
  • Amanda Louise Jacob
  • Samuel J Sober

National Institutes of Health (R01- NS084844)

  • James N McGregor
  • Abigail L Grassler
  • Amanda Louise Jacob
  • Samuel J Sober

National Institutes of Health (R01- NS099375)

  • James N McGregor
  • Abigail L Grassler
  • Amanda Louise Jacob
  • Samuel J Sober

Simons Foundation (Emory International Consortium on Motor Control)

  • Samuel J Sober

Howard Hughes Medical Institute

  • Paul I Jaffe
  • Michael S Brainard

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jesse H Goldberg, Cornell University, United States

Ethics

Animal experimentation: All experimental protocols were approved by the Emory University and UC San Francisco Institutional Animal Care and Use Committees (protocol #201700359)

Version history

  1. Received: November 19, 2021
  2. Preprint posted: December 10, 2021 (view preprint)
  3. Accepted: September 14, 2022
  4. Accepted Manuscript published: September 15, 2022 (version 1)
  5. Version of Record published: September 29, 2022 (version 2)

Copyright

© 2022, McGregor et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,219
    views
  • 300
    downloads
  • 11
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. James N McGregor
  2. Abigail L Grassler
  3. Paul I Jaffe
  4. Amanda Louise Jacob
  5. Michael S Brainard
  6. Samuel J Sober
(2022)
Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain
eLife 11:e75691.
https://doi.org/10.7554/eLife.75691

Share this article

https://doi.org/10.7554/eLife.75691

Categories and tags

Further reading

    1. Neuroscience

    Serotonergic amplification of odor-evoked neural responses maps onto flexible behavioral outcomes

    Yelyzaveta Bessonova, Baranidharan Raman
    Research Article

    Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

    1. Neuroscience

    MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

    Olivier Codol, Jonathan A Michaels ... Paul L Gribble
    Tools and Resources

    Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

    1. Neuroscience

    Cerebellar nuclei cells produce distinct pathogenic spike signatures in mouse models of ataxia, dystonia, and tremor

    Meike E van der Heijden, Amanda M Brown ... Roy V Sillitoe
    Research Article

    The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.