An adapting auditory-motor feedback loop can contribute to generating vocal repetition

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition.

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences

Removal of auditory feedback in Bengalese finches by deafening reduces peak repeat counts.

<p>a: Example spectrograms and rectified amplitude waveforms (blue traces) for the song of one bird before (top) and after (bottom) deafening. Red dashed boxes demarcate the repeated syllables. b: Median repeat counts per song of the syllable from before deafening (black) and after deafening (red). Rotated probability distributions at the right hand side display the repeat counts across all recorded songs before (black) and after (red) deafening. c: Additional examples of repeat distributions pre- (black) and post- (red) deafening. For syllables that were repeated many times, deafening caused sharp reductions in repetitions, resulting in repeat number distributions that are more Markovian (upper graphs). Deafening had less of an effect on syllables that were repeated fewer times (lower graphs). d: Deafening results in a significant decrease in the peak repeat numbers. Individual syllables are in black (overlapping points are vertically shifted for visual clarity), median across syllables is in red. (Wilcoxon sign-rank test, <i>p</i> < 10⁻², <i>N</i> = 19). e: Peak repeat numbers before deafening vs. the differences in peak repeat numbers before and after deafening. Red dots correspond to syllables and black line is from linear regression. Larger decreases in peak repeat numbers for syllables that were repeated many times before deafening (<i>R</i>² = 0.81, <i>p</i> < 10⁻⁷, <i>N</i> = 19).</p

Sigmoidal adaptation model fits diverse repeat number distributions of Bengalese finch songs.

<p>a: Six example Bengalese finch repeat count histograms (grey bars) and the best-fit model distributions (red lines). Peak repeat count increases from left-to-right and down columns. Distribution marked with (*) provide two examples of repeat distributions that have clear double peaks. For these cases, the peaks at repeat number 1 are excluded. b: Scatter plot of fit error vs. benchmark error. Each red circle corresponds to the distribution for one repeated syllable from the song database. The fit errors are smaller than the benchmark errors in the vast majority of cases (86%).</p