Literature DB >> 23222734

A lightweight, headphones-based system for manipulating auditory feedback in songbirds.

Lukas A Hoffmann1, Conor W Kelly, David A Nicholson, Samuel J Sober.   

Abstract

Experimental manipulations of sensory feedback during complex behavior have provided valuable insights into the computations underlying motor control and sensorimotor plasticity(1). Consistent sensory perturbations result in compensatory changes in motor output, reflecting changes in feedforward motor control that reduce the experienced feedback error. By quantifying how different sensory feedback errors affect human behavior, prior studies have explored how visual signals are used to recalibrate arm movements(2,3) and auditory feedback is used to modify speech production(4-7). The strength of this approach rests on the ability to mimic naturalistic errors in behavior, allowing the experimenter to observe how experienced errors in production are used to recalibrate motor output. Songbirds provide an excellent animal model for investigating the neural basis of sensorimotor control and plasticity(8,9). The songbird brain provides a well-defined circuit in which the areas necessary for song learning are spatially separated from those required for song production, and neural recording and lesion studies have made significant advances in understanding how different brain areas contribute to vocal behavior(9-12). However, the lack of a naturalistic error-correction paradigm - in which a known acoustic parameter is perturbed by the experimenter and then corrected by the songbird - has made it difficult to understand the computations underlying vocal learning or how different elements of the neural circuit contribute to the correction of vocal errors(13). The technique described here gives the experimenter precise control over auditory feedback errors in singing birds, allowing the introduction of arbitrary sensory errors that can be used to drive vocal learning. Online sound-processing equipment is used to introduce a known perturbation to the acoustics of song, and a miniaturized headphones apparatus is used to replace a songbird's natural auditory feedback with the perturbed signal in real time. We have used this paradigm to perturb the fundamental frequency (pitch) of auditory feedback in adult songbirds, providing the first demonstration that adult birds maintain vocal performance using error correction(14). The present protocol can be used to implement a wide range of sensory feedback perturbations (including but not limited to pitch shifts) to investigate the computational and neurophysiological basis of vocal learning.

Entities:  

Mesh:

Year:  2012        PMID: 23222734      PMCID: PMC3564484          DOI: 10.3791/50027

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  16 in total

1.  Independent learning of internal models for kinematic and dynamic control of reaching.

Authors:  J W Krakauer; M F Ghilardi; C Ghez
Journal:  Nat Neurosci       Date:  1999-11       Impact factor: 24.884

2.  Interruption of a basal ganglia-forebrain circuit prevents plasticity of learned vocalizations.

Authors:  M S Brainard; A J Doupe
Journal:  Nature       Date:  2000-04-13       Impact factor: 49.962

Review 3.  Error correction, sensory prediction, and adaptation in motor control.

Authors:  Reza Shadmehr; Maurice A Smith; John W Krakauer
Journal:  Annu Rev Neurosci       Date:  2010       Impact factor: 12.449

4.  Performance variability enables adaptive plasticity of 'crystallized' adult birdsong.

Authors:  Evren C Tumer; Michael S Brainard
Journal:  Nature       Date:  2007-12-20       Impact factor: 49.962

5.  Voice F0 responses to pitch-shifted voice feedback during English speech.

Authors:  Stephanie H Chen; Hanjun Liu; Yi Xu; Charles R Larson
Journal:  J Acoust Soc Am       Date:  2007-02       Impact factor: 1.840

6.  Effects of perturbation magnitude and voice F0 level on the pitch-shift reflex.

Authors:  Hanjun Liu; Charles R Larson
Journal:  J Acoust Soc Am       Date:  2007-12       Impact factor: 1.840

Review 7.  Neural mechanisms for learned birdsong.

Authors:  Richard Mooney
Journal:  Learn Mem       Date:  2009-10-22       Impact factor: 2.460

Review 8.  Birdsong and human speech: common themes and mechanisms.

Authors:  A J Doupe; P K Kuhl
Journal:  Annu Rev Neurosci       Date:  1999       Impact factor: 12.449

9.  Central contributions to acoustic variation in birdsong.

Authors:  Samuel J Sober; Melville J Wohlgemuth; Michael S Brainard
Journal:  J Neurosci       Date:  2008-10-08       Impact factor: 6.167

10.  Adult birdsong is actively maintained by error correction.

Authors:  Samuel J Sober; Michael S Brainard
Journal:  Nat Neurosci       Date:  2009-06-14       Impact factor: 24.884

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  10 in total

1.  Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds.

Authors:  Baohua Zhou; David Hofmann; Itai Pinkoviezky; Samuel J Sober; Ilya Nemenman
Journal:  Proc Natl Acad Sci U S A       Date:  2018-08-20       Impact factor: 11.205

2.  Vocal generalization depends on gesture identity and sequence.

Authors:  Lukas A Hoffmann; Samuel J Sober
Journal:  J Neurosci       Date:  2014-04-16       Impact factor: 6.167

Review 3.  Variations on a theme: Songbirds, variability, and sensorimotor error correction.

Authors:  B D Kuebrich; S J Sober
Journal:  Neuroscience       Date:  2014-10-14       Impact factor: 3.590

4.  Application of the hierarchical bootstrap to multi-level data in neuroscience.

Authors:  Varun Saravanan; Gordon J Berman; Samuel J Sober
Journal:  Neuron Behav Data Anal Theory       Date:  2020-07-21

5.  A simple computational principle predicts vocal adaptation dynamics across age and error size.

Authors:  Conor W Kelly; Samuel J Sober
Journal:  Front Integr Neurosci       Date:  2014-09-29

6.  The Effects of Pitch Shifts on Delay-Induced Changes in Vocal Sequencing in a Songbird.

Authors:  MacKenzie Wyatt; Emily A Berthiaume; Conor W Kelly; Samuel J Sober
Journal:  eNeuro       Date:  2017-01-20

7.  Dopamine Depletion Affects Vocal Acoustics and Disrupts Sensorimotor Adaptation in Songbirds.

Authors:  Varun Saravanan; Lukas A Hoffmann; Amanda L Jacob; Gordon J Berman; Samuel J Sober
Journal:  eNeuro       Date:  2019-06-12

8.  Driving singing behaviour in songbirds using a multi-modal, multi-agent virtual environment.

Authors:  Leon Bonde Larsen; Iris Adam; Gordon J Berman; John Hallam; Coen P H Elemans
Journal:  Sci Rep       Date:  2022-08-04       Impact factor: 4.996

9.  Dopaminergic Contributions to Vocal Learning.

Authors:  Lukas A Hoffmann; Varun Saravanan; Alynda N Wood; Li He; Samuel J Sober
Journal:  J Neurosci       Date:  2016-02-17       Impact factor: 6.167

10.  An automated procedure for evaluating song imitation.

Authors:  Yael Mandelblat-Cerf; Michale S Fee
Journal:  PLoS One       Date:  2014-05-08       Impact factor: 3.240

  10 in total

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