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

Avian song system and branched chain network with adapting auditory feedback.

<p>a: Diagram of the avian song system. HVC is a sensory-motor integration area that receives auditory input from high-level auditory nuclei such as NIf (nucleus interfacealus), and sends temporally precise motor controls signals to nucleus RA (robust nucleus of the arcopallium), which projects to the vocal brainstem areas. There is a pre-motor latency of 30–50 ms (Δ<i>T</i> Motor) between activity in HVC and subsequent vocalization. Additionally, there is a latency of 15–20 ms (Δ<i>T</i> Auditory) for auditory activity to reach HVC. This makes for a total auditory-motor latency between pre-motor activity and resulting auditory feedback of 45–70 ms. b: Example of a branch point in a probabilistic sequence (left). Syllable ‘a’ can transition to either syllable ‘b’ or ‘c’. Such probabilistic sequences can be produced by a branched chain network (right). Here, each syllable is produced by a syllable-chain, in which groups of HVC_RA neurons (grey dots in red ovals, grouped in grey rectangles for a given syllable) are connected unidirectionally in a feed-forward chain (black lines with triangles are excitatory connections). The end of chain-a connects to the beginning of chain-b and chain-c. Spike activity propagates through chain-a and drives downstream neurons in RA to produce syllable a. At the end of chain-a, the activity continues to chain-b or chain-c via the branched connections. Only one syllable chain can be active at a time, as enforced by winner-take-all mechanisms mediated through local feedback inhibition from the HVC_I neurons (red lines are inhibitory connections). c: The branched chain network with adapting auditory feedback for generating repeating sequences of syllable ‘b’. The end of chain-b reconnects to its beginning and to chain-c. Auditory feedback from syllable ‘b’ is applied to chain-b, and biases the repeat probability when the activity propagates to the branching point. The feedback is weakened as syllable ‘b’ repeats due to use-dependent synaptic depression which models stimulus-specific adaptation of the auditory signal.</p

Non-Markovian repeated syllables are loudest and evoke the largest HVC auditory responses.

<p>a: Mean ±s.d. amplitude waveforms for a non-Markovian repeated syllable (black), a Markov repeated syllable (red), and an intro note (grey) from the songs of one bird. b: Mean ±s.e. normalized peak amplitudes of song vocal elements. Intro notes (Intro), non-repeated syllables (NR), Markov-repeated syllables (MR, peak repeat number = 1), and non-Markovian repeated syllables (nMR, peak repeat number > 1). non-Markovian repeated syllables are significantly louder than other vocalizations (<i>p</i> < 10⁻³, <i>p</i> < 0.01, Wilcoxon sign-rank test, Bonferroni corrected for <i>m</i> = 3 comparisons). c: Scatter plot of normalized auditory responses to a syllable as a function of the normalized amplitude of that syllable. Black line is from regression (<i>R</i>² = 0.30;<i>p</i> < 10⁻³, <i>N</i> = 40 syllables). d: Paired comparison of normalized auditory responses to non-repeated syllables (NR) and non-Markovian repeated syllables (nMR). Repeated syllables illicit larger auditory responses. (<i>p</i> < 0.01, Wilcoxon sign-rank test, <i>N</i> = 11 sites). Circles: data; square: median.</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

Sigmoidal adaptation model of repeats and model predictions.

<p>a: Six example repeat count histograms (black bars) from the neural network simulations with adapting auditory feedback and the best fit distributions from the sigmoidal adaptation model (red lines). The decay constants of the auditory feedback and the syllable lengths are varied to produce different repeat number distributions. Syllable lengths are changed by altering the number of groups per chain. All fit errors are smaller than benchmark errors. b: Best fit geometric adaptation models for the first histogram in a. With geometric adaptation, the probably of a state repeating is decreased by a constant factor with each consecutive repeat (Materials and Methods): (i) single state; (ii) two-states, both repeating; (iii) multiple-states, only final state repeating. In all cases, numbers on arrows are initial transition probabilities while the number in parenthesis is the constant adaptation factor. c: Comparison of model fits. Red is the best fit of the sigmoidal adaptation model with one state. Other colors are best fits of the corresponding models in b. The sigmoidal adaptation model provides a superior fit while only requiring a single state. d: Peak repeat number as a function of the initial auditory feedback strength and the adaptation strength generated using the sigmoidal adaptation model. The peak repeat number increases for increasing initial feedback strength and decreases for increasing adaptation strength. For a given adaptation strength, there is a threshold feedback strength at which repeat distributions become non-Markovian (i.e. peak repeat number > 1).</p