We tested whether non-auditory feedback can drive vocal learning (Figure 1b, Experiment 1) by providing mild, pitch-contingent cutaneous stimulation through a set of wire electrodes on the scalps of adult songbirds. Before initiating cutaneous stimulation training, we continuously recorded song without providing any feedback for 3 days (baseline) (Figure 2a). Every day, songbirds naturally produce many renditions of song, which consist of repeated patterns of unique vocal gestures, called syllables (Figure 2b, top). For one ‘target’ syllable in each experimental subject, we quantified rendition-to-rendition variability in the fundamental frequency (which we refer to here as ‘pitch’) of each occurrence of this syllable on the final baseline day (Figure 2b, top). To differentially reinforce the pitch of a target syllable, we determined a range of pitches within this baseline distribution (either all pitches above the 20th percentile or all pitches below the 80th percentile), and then triggered the delivery of cutaneous stimulation in real time (within 40 ms of syllable onset) when the pitch of the target syllable fell within this range (Figure 2b, bottom). We performed this pitch-contingent cutaneous stimulation training continuously for 3 days. Note that the birds could choose not to sing in order to avoid triggering any cutaneous stimulation, and we carefully monitored animal subjects for any signs of distress (see ‘Materials and methods’).

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For example, in one experiment (shown in Figure 2a–d, Figure 2—source data 1), cutaneous stimulation was triggered on every rendition of the target syllable that had a pitch above 2.13 kHz (the 20th percentile of the baseline distribution) for 3 days. In this example experiment, the bird gradually changed the pitch of the targeted syllable downward (the adaptive direction), such that cutaneous stimulation was triggered less frequently (Figure 2c). In other experiments where the adaptive direction of pitch change is upward, we triggered cutaneous stimulation whenever the target syllable pitch was below the 80th percentile of this distribution. In the example experiment, at the start of the first day of cutaneous stimulation training, 80% of syllable renditions resulted in cutaneous stimulation and 20% of syllable renditions resulted in escapes. On the third (final) day of cutaneous stimulation training, escapes occurred on over 60% of target syllable renditions and the entire distribution of pitches had changed significantly in the adaptive direction, indicating that a significant amount of vocal learning occurred in this example experiment (Figure 2d; two-sample Kolmogorov–Smirnov test to assess the difference between baseline and end of cutaneous stimulation training, p=1.1776e-12). We then stopped triggering cutaneous stimulation and continued to record unperturbed song for six additional days (washout). After 6 days of washout, there was no significant difference between the distribution of target syllable pitches at the end of washout compared to baseline (Figure 2d; two-sample Kolmogorov–Smirnov test, p=0.606). For analysis of washout across all experiments, see Figure 2—figure supplement 1.

In order to assess whether non-auditory feedback is sufficient to drive vocal learning across multiple songbirds, we first measured the adaptive pitch change (in semitones) for each individual experiment. Semitones provide a normalized measure of pitch change such that a one semitone change corresponds to a roughly 6% change in the absolute frequency of an acoustic signal (see Equation 1). We employed a hierarchical bootstrap approach to measure SEM and assess significance (see ‘Materials and methods’; Saravanan et al., 2020; Saravanan et al., 2019) since this method more accurately quantifies the error in hierarchical data (e.g., many renditions of a target syllable collected across multiple birds). We found that the mean pitch (in semitones) of the target syllables showed a significant, adaptive change from baseline on days 2 and 3 of cutaneous stimulation training (Figure 2e; the probability of resampled mean pitch on cutaneous stimulation training days 2 and 3 lesser than or equal to zero was P_boot < 0.0010, limit due to resampling 10⁴ times, n = 13 experiments in 12 birds, one bird underwent two cutaneous stimulation experiments; Figure 2—source data 1). This demonstrates that non-auditory feedback is sufficient to drive vocal learning in adult songbirds. In all individual experiments where an upward pitch change resulted in less frequent triggering of cutaneous stimulation, the birds changed their pitch in the adaptive (upward) direction, and in all experiments where a downwards pitch change resulted in less frequent triggering of cutaneous stimulation, the birds changed their pitch in the adaptive (downward) direction (Figure 2—figure supplement 2a, Figure 2—source data 1).

To further characterize cutaneous stimulation training and compare this form of learning to well-established vocal learning paradigms, we performed multiple learning experiments – one cutaneous stimulation and one white noise – in 8 out of the 12 individual birds from this dataset where the implanted electrode wires remained intact for a long enough time to perform multiple sets of experiments (Figure 2a). To account for the potential influence of multiple trainings in the same individual birds on magnitude of learning, we randomized the order of white noise training and cutaneous stimulation training for the birds that underwent both training paradigms. We also included six LMAN sham-operated birds from a later set of experiments in this particular analysis. We did so because the sham-operated birds had intact song systems and underwent both cutaneous stimulation and white noise training. Also, we found no statistically significant difference between the magnitude of learning by the end of training in birds that did not undergo craniotomies for LMAN, 6-OHDA, or sham lesions compared with the magnitude of learning in birds that received sham LMAN lesions for either white noise experiments (two-sample t-test, p=0.779) or cutaneous stimulation experiments (two-sample t-test, p=0.148).

Consistent with prior studies (Ali et al., 2013; Hoffmann et al., 2016; Tumer and Brainard, 2007), by the end of white noise training, the adaptive pitch change (in semitones) across all white noise experiments performed in unoperated birds (birds that had wire electrodes surgically implanted but received no invasive brain procedures like sham operations) was significantly greater than baseline (zero) (Figure 2—figure supplement 3a; the probability of resampled mean pitch on all three cutaneous stimulation training days lesser than or equal to zero was P_boot < 0.0010; Figure 2—source data 1). In the separate experimental group of birds that underwent sham operations, we also observed significant adaptive pitch changes in response to white noise bursts, as expected (Figure 2—figure supplement 3b; the probability of resampled mean pitch on all three cutaneous stimulation training days lesser than or equal to zero was P_boot < 0.0010; Figure 2—source data 1). There was significant individual variability in learning magnitudes (adaptive pitch change at the end of training) during cutaneous stimulation and white noise experiments (Figure 2f, Figure 2—source data 1). We found no systematic differences between learning magnitude during cutaneous stimulation training and the learning magnitude during white noise training (Figure 2f; paired t-test, p=0.313). These results suggest that non-auditory stimuli can drive vocal learning as effectively as auditory stimuli.

To confirm that cutaneous stimulation learning was truly driven by the non-auditory stimulus and not by an unintentional, acute change in vocal output caused by the cutaneous stimulation, we measured the syllable features of interleaved ‘catch’ trials, where cutaneous stimulation was randomly withheld (see ‘Materials and methods’) on each day of cutaneous stimulation training. For each experiment, we normalized the pitch of each catch trial from each day of training to the mean pitch of all trials where cutaneous stimulation was provided. We excluded any experiments where the total number of catch trials was less than 10. In every case, the normalized catch trials did not differ significantly from 1, indicating that the pitch of catch trials was highly similar to trials where cutaneous stimulation was provided (Figure 2—figure supplement 4a; t-test, 0.071 < p < 0.997 for each experiment; Figure 2—source data 1). For comparison, we also performed the same analysis on randomly selected trials from a day of baseline recording, where cutaneous stimulation was not provided on any trials (Figure 2—figure supplement 4a, Figure 2—source data 1). There was no significant difference between this dataset and the normalized catch trials (paired t-test, p=0.339). We repeated this analysis for other syllable features, such as syllable duration, sound amplitude, and spectral entropy. In all cases, we did not see a robust, acute change in song performance caused by the cutaneous stimulation. To ensure that the cutaneous stimulation on the scalp did not drive learning through an unexpected influence on brain activity in dorsal auditory areas of the pallium, we implanted the wire electrodes in the neck instead of the scalp in 7 out of 12 birds used in these experiments. The magnitude of vocal learning did not differ between the two groups of birds on any day of training (0.679 < P_boot < 0.891). To demonstrate that the ability to learn to adaptively shift the pitch of the target syllable is consistent across individual birds, we have plotted the results of 6 out of the 12 birds used in this dataset in Figure 2—figure supplement 5 and Figure 2—figure supplement 6 (Figure 2—source data 2). Also, we reanalyzed this dataset by measuring syllable pitch from syllables produced in the evening (6 PM to 8 PM) instead of in the morning, and we found no significant difference in the magnitude of learning between song collected between 10 AM and noon and song collected between 6 PM and 8 PM on any day of training (Figure 2—figure supplement 7, 0.167 < P_boot < 0.951 on all days of training; Figure 2—source data 3). Taken together, these results indicate that the gradual, adaptive pitch shift is driven by non-auditory cutaneous stimulation and not by other unintentional effects of the stimulation, the methodology of wire implantation, or the time window of song analysis.

We next investigated the neural circuitry that processes non-auditory feedback to drive vocal learning. To assess whether the AFP is required for non-auditory vocal learning, we measured the effect of lesions of LMAN, the output nucleus of the AFP, on learning magnitude in response to non-auditory feedback (Figure 1b, Experiment 2). We performed cutaneous stimulation training experiments in the same individual birds before and after bilateral, electrolytic LMAN lesions (n = 5 birds, and one additional bird that only underwent postlesion training) or sham operations (Figure 3a, n = 5 birds). To perform cutaneous stimulation training in this group of experiments, we used the same protocol described previously, except we extended the period of cutaneous stimulation training an additional 2 days. During this extended training period, we set a new pitch threshold each morning to drive even greater amounts of learning (‘staircase’ training, see ‘Materials and methods’). We did so in case LMAN lesions differentially impacted small and large magnitudes of learning, In adult songbirds with intact song systems (prelesion), such staircase training drove significant amounts of learning (Figure 3c).

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We then lesioned LMAN and performed postlesion white noise training across conditions (LMAN lesion and sham) (Figure 2—figure supplement 3b). The efficacy of LMAN lesions was confirmed both by the presence of a characteristic reduction in the trial-to-trial variability of syllable pitch, analyzed across all labeled song syllables produced by the birds (including the target syllable used in learning experiments) (Figure 3b, Figure 3—figure supplement 1a, LMAN lesions p=0.002, sham lesions p=0.911, paired t-tests; Figure 3—source data 1; Kao et al., 2005; Kao and Brainard, 2006; Hampton et al., 2009), as well as by post-hoc histological measurements (see ‘Materials and methods’ and Figure 3—figure supplement 2). Following LMAN lesions, songbirds did not significantly change the pitch of the target syllable from baseline (zero) (the probability of resampled mean pitch on the final 4 days of training lesser than or equal to zero was P_boot > 0.223, n = 3). In contrast, following sham lesions, birds significantly changed the pitch of the target syllable in the adaptive direction (the probability of resampled mean pitch on the final 4 days of training days lesser than or equal to zero was P_boot < 0.0010, n = 5). This indicates that LMAN lesions induced significant deficits in auditory-driven vocal learning, consistent with previous work (Hisey et al., 2018).

LMAN lesions also significantly impaired non-auditory vocal learning. Prelesion, songbirds adaptively changed the pitch of the target syllable away from baseline in response to non-auditory feedback (the probability of resampled mean pitch on each day of cutaneous stimulation training lesser than or equal to zero was P_boot < 0.0010) (Figure 3d, Figure 3—source data 2). Postlesion, non-auditory vocal learning was abolished in those same birds (the probability of resampled mean pitch on each of the final 4 days of training lesser than or equal to zero was 0.297 < P_boot < 0.660, where 0.025 < P_boot < 0.975 indicates no significant difference, n = 6 birds; one bird from this group did not undergo prelesion experimentation) (Figure 3d, Figure 3—source data 2). Learning magnitude prelesion was significantly greater compared to learning magnitude postlesion (P_boot < 0.007 on each of the final 4 days of training). We observed significant learning during cutaneous stimulation training in both pre- and postsham-lesioned datasets (Figure 3e, for both presham and postsham datasets, the probability of resampled mean pitch on each day of cutaneous stimulation training lesser than or equal to zero was P_boot < 0.0010, n = 5 birds; Figure 3—source data 3). Also, the learning magnitudes during cutaneous stimulation training did not significantly differ in pre- vs. postsham datasets (the probability of resampled mean pitch of presham data on each day of training lesser than or equal to resampled mean pitch of postlesion data was 0.120 < P_boot < 0.524). The amount of pitch change during cutaneous stimulation training for each individual experiment is shown in Figure 2—figure supplement 2c, d.

We also directly compared the lesion-induced change in learning magnitudes between conditions (LMAN lesion vs. sham) (Figure 3—figure supplement 1b and c). First, we calculated learning magnitude at the end of the fixed threshold training period across conditions. The lesion-induced change in learning magnitude (post – pre) for LMAN-lesioned birds was significantly greater than that for sham-operated birds (Figure 3—figure supplement 1b; two-sample Kolmogorov–Smirnov test, p=0.036, n = 5 birds in both groups). Next, we calculated learning magnitude at the end of the extended ‘staircase’ portion of cutaneous stimulation training across conditions. The lesion-induced change in learning magnitude (post – pre) for LMAN-lesioned birds calculated at this time point was also significantly greater than for sham-lesioned birds (Figure 3—figure supplement 1c; two-sample Kolmogorov–Smirnov test, p=0.004, n = 5 birds in both groups). These results show that LMAN is required for non-auditory vocal learning in adult songbirds, indicating that both auditory and non-auditory sensory feedback engage the AFP to drive adaptive changes to song.

We next assessed dopaminergic contributions to non-auditory vocal learning (Figure 1b, Experiment 3). Learning magnitude during cutaneous stimulation training was assessed before and after bilaterally lesioning dopaminergic projections in Area X, the basal ganglia nucleus of the AFP, in individual songbirds (Figure 4a, n = 5 birds). Selective lesions of dopaminergic projections in Area X were performed via bilateral 6-OHDA injections in Area X (see ‘Materials and methods’), and the effectiveness of the 6-OHDA injections at lesioning dopaminergic innervation in Area X was quantified (Figure 4—figure supplement 1). This approach has previously been shown to selectively lesion dopaminergic inputs to Area X without damaging non-dopaminergic cells (Hoffmann et al., 2016; Saravanan et al., 2019).

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We again measured the variability of syllable pitch pre- and postlesion by calculating syllable CV. Dopaminergic lesions in Area X did not induce a significant change in syllable CV (Figure 4b; paired t-test, p=0.397; Figure 4—source data 1). Sham operations also did not induce a significant change in syllable CV (Figure 4b; paired t-test, p=0.531). The lesion-induced changes in syllable CV (post – pre) were not significantly different for 6-OHDA-lesioned birds than for sham-lesioned birds (Figure 3—figure supplement 1d; two-sample Kolmogorov–Smirnov test, p=0.054). This finding is consistent with prior work using similar 6-OHDA injections to lesion dopaminergic input to Area X Hoffmann et al., 2016. Prior work has suggested a link between dopamine in songbird AFP and the generation of variability in syllable pitch in adult songbirds (Murugan et al., 2013; Sasaki et al., 2006; Leblois et al., 2010). It is likely that the dopamine lesion methodology we used, which spares about 50% of the dopaminergic input to Area X Hoffmann et al., 2016, is insufficient to impair dopamine-mediated generation of syllable variability. The result that these dopamine lesions do not alter vocal variability establishes that any learning deficits observed following lesions of AFP circuits are not simply due to decreased pitch variability.

Depletion of dopaminergic input to Area X significantly impaired adaptive, non-auditory vocal learning. Prelesion, songbirds adaptively changed the pitch of the target syllable during cutaneous stimulation training (the probability of resampled mean pitch on each of the final 4 days of cutaneous stimulation training lesser than or equal to zero was P_boot < 0.010) (Figure 4c, Figure 4—source data 2). Postlesion, these same birds were not able to adaptively change the pitch of the target syllable during cutaneous stimulation training (the probability of resampled mean pitch on each of the first 4 days of training lesser than or equal to zero was 0.067 < P_boot < 0.553; the probability of resampled mean pitch on the final day of training greater than or equal to zero was P_boot < 0.0010, n = 5 birds). Learning magnitude prelesion was significantly greater compared to learning magnitude postlesion (the probability of resampled mean pitch from prelesion dataset on each of the final 3 days of cutaneous stimulation training lesser than or equal to resampled mean pitch from postlesion dataset was P_boot < 0.0010). Both pre- and postsham, songbirds displayed significant amounts of learning during cutaneous stimulation training (Figure 4d; the probability of resampled mean pitch from the presham dataset on each day other than day 2 of cutaneous stimulation training lesser than or equal to zero was P_boot < 0.0010; the probability of resampled mean pitch from the postsham dataset on each day of cutaneous stimulation training lesser than or equal to zero was P_boot < 0.0010, n = 3 birds; Figure 4—source data 3). Also, the learning magnitudes during cutaneous stimulation training did not significantly differ pre- vs. postsham (the probability of resampled mean pitch of presham data on each day of training lesser than or equal to resampled mean pitch of postlesion data was 0.653 < P_boot < 0.931). These results demonstrate that dopaminergic input to Area X is required for adaptive changes in vocal output in response to non-auditory signals. The amount of pitch change during cutaneous stimulation training for each individual experiment is shown in Figure 2—figure supplement 2d and e.

To deliver non-auditory feedback signals to freely behaving songbirds during ongoing song performance, we first performed a surgery prior to any experimentation. Stainless steel wires were uninsulated at the tip (2–4 mm) and implanted subcutaneously on the bird’s scalp. The approximate location of the scalp electrodes was 4.47 mm lateral and 6.3 mm anterior, relative to Y₀, far from the coordinates used for targeting auditory pallium, which are 1.1 mm anterior and 0.7 mm lateral, relative to Y₀, and 1.5 mm ventral from the surface of the brain (Spool et al., 2021). In 7 out of all 28 birds used across all experiments performed, wires were implanted intramuscularly in the birds' necks instead of on their scalps. The wires were soldered onto a custom-made circuit board that, during surgery, was placed on the bird’s skull using dental cement. The circuit was connected to an electric stimulator (A-M Systems Isolated Pulse Stimulator), which produced pitch-contingent electrical currents through the wires implanted on the bird. We set the duration of cutaneous stimulation to 50 ms, which was a long enough duration to overlap with a large portion of the targeted syllable, yet a short enough duration to avoid interfering with following song syllables. We typically set the magnitude of electric current used for producing the cutaneous stimulation to 100–350 μA, which is behaviorally salient (the first few instances of cutaneous stimulation interrupt song), yet subtle enough as to not produce any body movements or signs of distress. Importantly we tried to match the same level of behavioral saliency across birds and, although the magnitude of electric current varied by a large amount across individual birds, it only varied by small amounts (<20 μA) pre- vs postlesion in those sets of experiments. Stimulations typically occurred within 20–30 ms of target syllable onset. Acute effects of electrical cutaneous stimulation on song structure, such as pitch, sound amplitude, entropy, or syllable sequence, were assessed to ensure these non-auditory stimuli produced no immediate, systematic, acoustic effects. This ensures that any observed gradual changes to song structure in response to cutaneous stimulation are due to non-auditory learning.

Experimental testing of vocal learning was performed by driving adaptive changes in the fundamental frequency (pitch) of song syllables. To do so, we delivered pitch-contingent, non-auditory feedback (mild cutaneous electrical stimulation) to freely behaving songbirds in real time during song performance. We followed the same experimental protocols as experiments using white noise feedback to drive vocal learning (Hoffmann et al., 2016; Tumer and Brainard, 2007), except we used cutaneous stimulation instead of white noise bursts. After surgically implanting the fine-wire electrodes, we recorded song continuously for 3 days without providing any experimental feedback (cutaneous stimulation or white noise bursts). We refer to this period as ‘baseline’ (Figure 2a).

On the last (third) day of baseline, we measured the pitch of every rendition of the target syllable sung between 10 AM and 12 PM. We set a fixed pitch threshold based on the distribution of these pitches, such that we would provide sensory feedback only when the pitch of a rendition of the target syllable was above the 20th percentile of the baseline distribution (‘hit’), and all renditions outside of this range did not trigger any feedback (‘escape’). In this case, an adaptive vocal change would therefore be to change the pitch of the target syllable down, thereby decreasing the frequency of triggering cutaneous stimulation. In other experiments, we triggered feedback on all renditions below the 80th percentile of the baseline pitch distribution. In this case, an adaptive vocal change would be to change the pitch of the target syllable up. For each experiment, we randomly selected which of these two contingencies we employed so we could assess bidirectional adaptations in vocal motor output. In a subset of experiments, we used the 90th percentile and 10th percentile pitch values to set the pitch threshold. Importantly, we also randomly withheld triggering feedback on 10% of syllable renditions, regardless of syllable pitch or the experimental pitch contingency. This allows us to compare syllable renditions that did or did not result in cutaneous stimulation to assess any acute effects of this form of feedback on syllable structure.

At 10 AM on the fourth day of continuous song recording, we began providing pitch-contingent cutaneous stimulation in real time, targeted to specific song syllables sung within a specified range of pitches. We refer to this time period as ‘cutaneous stimulation training’ (Figure 2a). We used custom LabVIEW software to continuously record song, monitor song for specific elements indicative of the performance of the target syllable, perform online, rapid pitch calculation, and trigger feedback in real time. The computers running this software were connected to an electric stimulator. When the electric stimulator received input from the LabVIEW software, it would then trigger a 50 ms burst of electric current through the implanted wire electrodes. During cutaneous stimulation training, we continuously recorded song and provided pitch-contingent cutaneous stimulation at the set fixed pitch threshold for 3 days. During these 3 days, every time the bird sang within the ‘hit’ range, a mild cutaneous stimulation was immediately triggered.

After 3 days of cutaneous stimulation training, we stopped providing cutaneous stimulation but continued recording unperturbed song for six additional days. We refer to this period as ‘washout’ (Figure 2a). During washout, we consistently observed spontaneous pitch restoration back to baseline across all experiments, which is in congruence with results from numerous white noise learning experiments (Hoffmann et al., 2016; Andalman and Fee, 2009; Tumer and Brainard, 2007). This allows for multiple experiments to be performed from similar baseline conditions in the same individual songbird.

In 14 out of all 28 birds used throughout this study, we performed both white noise training and cutaneous stimulation training in the same individual birds (Figure 2a). After the end of cutaneous stimulation training and 6 days of washout (when the pitch of the target syllable had restored to baseline levels), we performed the exact same experimental protocol, but we used white noise feedback instead of cutaneous stimulation. We could then compare learning in response to two different sources of sensory feedback in the same individual subject. We also sometimes reversed the order of experimentation by performing white noise experiments first and cutaneous stimulation experiments second. The order of experimentation was randomly decided for each songbird before beginning any white noise or cutaneous stimulation training.

For all LMAN lesion (Figure 3a) and 6-OHDA lesion experiments (Figure 4a), we performed a cutaneous stimulation training experiment prelesion. After 6 days of washout, we then performed surgery to lesion the neural circuit of interest. We then performed another cutaneous stimulation experiment in the same individual bird using the exact same protocol we used prelesion. For all of these lesion cutaneous stimulation experiments, we used the aforementioned cutaneous stimulation training paradigm, but with one slight alteration: we extended the number of days of cutaneous stimulation training and introduced a new methodology for setting the pitch threshold on these extended days of training. We still set a fixed pitch threshold based on analysis of the pitch distribution from the final day of baseline and performed 3 days of cutaneous stimulation training using this fixed pitch threshold. We refer to this portion of the lesion experiments as ‘fixed’ because the pitch threshold for determining whether a cutaneous stimulation was provided remained the same for all 3 days. Rather than stopping cutaneous stimulation training at this point, we instead continued providing pitch-contingent cutaneous stimulation for an additional 1–5 days. In the morning (at 10 AM) on each of these extended days of cutaneous stimulation training, we changed the pitch threshold to the 20th or 80th percentile (consistent with the initial contingency) of the pitch distribution of all renditions of the target syllable sung between 8 AM to 9:30 AM on that same day. As the bird changed the pitch of the target syllable in the adaptive direction, the new pitch thresholds continued to be set further and further in the adaptive direction to drive greater amounts of learning. We refer to these additional days as ‘staircase.’ After 1–5 days of staircase training, we stopped providing cutaneous stimulation and began the washout portion of the experiment. We used this experimental approach for both prelesion and postlesion experiments in our LMAN, 6-OHDA, and sham datasets. Importantly, although the number of days of staircase varied between individual birds, for each individual bird we matched the same number of prelesion days of staircase and postlesion days of staircase to ensure that, in both experimental conditions, the bird had an equivalent amount of time and opportunity to learn.

Custom-written MATLAB software (The MathWorks) was used for song analysis. On each day of every experiment, we quantified important song features, such as the pitch, sound amplitude, and spectral entropy, of all renditions of the targeted syllable produced between 10 AM and 12 PM. We did so to account for potential circadian effects on song production. We also reassessed our results (shown in Figure 2) by analyzing only syllable renditions produced between 6 PM and 8 PM using new methods for automated labeling of song syllables (Cohen et al., 2022). We found no statistically significant difference in learning magnitudes between the two forms of analysis (Figure 2—figure supplement 7a, 0.167 < P_boot < 0.951 on all days of training). To ensure a level of consistency in the number of target syllable renditions measured on each day of an experiment, and to have a minimum number of syllable renditions necessary to get an accurate measure of average syllable pitch, we checked that at least 30 renditions of the target syllable were sung within the 10 AM to 12 PM window. If there were less than 30 renditions of the target syllable, we extended the time window for song analysis by 1 hr in both directions (9 AM to 1 PM) and then reassessed to see if there were at least 30 syllable renditions. If not, we continued this process of extending the time window by 1 hr until 30 song renditions were in that day’s dataset. Daily targeting sensitivity (hit rate) and precision (1 – false-positive rate) were measured in all experiments to ensure accurate targeting of the specific target syllable (and not accidentally targeting different song syllables). During the pitch-contingent feedback portion of the experiment, a subset (10%) of randomly selected target syllables did not trigger feedback, regardless of syllable pitch. These ‘catch trials’ allowed for the quantification and comparison of syllable features, such as pitch, sound amplitude, and entropy between trials when feedback was provided and trials when feedback was not provided. Pitch changes were quantified in units of semitones as follows:

where s is the pitch change (in semitones) of the syllable, h is the average pitch (in Hertz) of the syllable, and b is the average baseline pitch (in Hertz) of the syllable.

We compared pitch variability pre- and postlesion using methods described in prior literature (Kao et al., 2005; Kao and Brainard, 2006; Hampton et al., 2009.) We analyzed all song renditions (within the 10 AM to 12 PM time window) performed on the final day of baseline prelesion and on the final day of baseline postlesion. We did so in our LMAN lesion experimental group as well as our 6-OHDA lesion experimental group. To measure the variability in pitch of the song syllables, we calculated the coefficient of variation (CV) for the pitch of each syllable using the following formula: CV = (Standard Deviation/Mean) * 100.

Birds were anesthetized under ketamine and midazolam and were mounted in a stereotax. The beak angle was set to 20° relative to the surface level of the surgery table. For stereotactic targeting of specific brain regions (in this case, LMAN), anterior-posterior (AP) and medial-lateral (ML) coordinates were found relative to Y₀, a visible anatomical landmark located at the posterior boundary of the central venous sinus in songbirds. Dorsal-ventral (DV) coordinates were measured relative to the surface of the brain. Bilateral craniotomies were made at the approximate AP coordinates 4.9–5.7 mm and ML coordinates 1.5–2.5 mm. A lesioning electrode was then inserted 1.9–2.1 mm below the brain surface. These stereotactic coordinates targeted locations within LMAN. We then passed 100 μA of current for 60–90 s at 5–6 locations in LMAN in both hemispheres in order to electrolytically lesion the areas. This methodology was based on prior work involving LMAN lesions and LMAN inactivations (Ali et al., 2013; Andalman and Fee, 2009; Kao et al., 2005; Kao and Brainard, 2006; Hampton et al., 2009; Warren et al., 2011). In sham-operated birds, we instead performed small lesions in brain areas dorsal to LMAN. Again, this was consistent with methodology from prior studies (Ali et al., 2013; Kao et al., 2005; Kao and Brainard, 2006).

Birds recovered within 2 hr of surgery and began singing normally (at least 30 renditions of target syllable within 2 hr) typically 3–8 days after surgery. The number of songs sung per day did not differ significantly pre- vs. postlesion (paired t-test, p=0.249).

Behavioral measures indicated that LMAN was effectively lesioned in the birds in the LMAN lesion dataset. LMAN lesions in adult songbirds produce a significant decrease in the trial-to-trial variability of song syllable pitch (Kao et al., 2005; Kao and Brainard, 2006; Hampton et al., 2009). To assess lesion-induced changes in the variability of syllable pitch between conditions (LMAN lesion and sham), we calculated the CV of syllable pitch pre- and postlesion. We found that LMAN lesions induced a significant decrease in pitch CV (Figure 3b; paired t-test). Sham operations did not induce a significant change in syllable CV (Figure 3b; paired t-test, p=0.911). The lesion-induced changes in syllable CV (post – pre) were significantly greater than changes to CV in sham-lesioned controls (Figure 3—figure supplement 1a; two-sample Kolmogorov–Smirnov test, p=0.003).

Lesions were confirmed histologically using cresyl violet staining after completion of behavioral experimentation. In tissue from sham-operated birds, we identified Area X and LMAN based on regions of denser staining as well as well-characterized anatomical landmarks (Karten et al., 2013). The histology methodology we employed followed previous literature involving LMAN lesions (Ali et al., 2013; Kao et al., 2005). We performed Nissl stains to stain for neuronal cell bodies in brain slices after experiments were complete (Figure 3—figure supplement 2a). We then calculated the optical density ratio of the region containing LMAN compared to background (a pallial region outside of LMAN) (Figure 3—figure supplement 2b; Hoffmann et al., 2016; Saravanan et al., 2019). The distribution of OD ratios from LMAN-lesioned tissue was significantly less than the OD ratios from sham-lesioned tissue (Figure 3—figure supplement 2c; two-sample Kolmogorov–Smirnov test, p<0.0010). This suggests that the density of neuronal cell bodies within LMAN was reduced following electrolytic lesions compared to following sham. Similar to a prior study, we also qualitatively assessed each slice of brain tissue to measure the percentage of intact LMAN remaining in the tissue (Ali et al., 2013). We found that all of the LMAN-lesioned birds had 80–100% of LMAN lesioned in both hemispheres.

Birds were anesthetized using ketamine and midazolam and were mounted in a stereotax, where the beak angle was set to 20° relative to the surface level of the surgery table. Isoflurane was used in later hours of the surgery to maintain an anesthetized state. Bilateral craniotomies were made above Area X from the approximate AP coordinates 4.5–6.5 mm and ML coordinates 0.75–2.3 mm relative to Y₀.

In each hemisphere, we inserted a glass pipette containing a 6-OHDA solution and made 12 pressure injections in a 3 mm × 4 mm grid between AP coordinates 5.1 mm and 6.3 mm, ML coordinates 0.9 mm and 2.2 mm, and the DV coordinate 3.18 mm relative to Y₀. Additional bilateral 6-OHDA injections were made at the AP coordinate 4.8 mm, ML coordinate ±0.8 mm, and DV coordinate 2.6 mm from the brain surface to lesion the most medial portion of Area X. Each injection consisted of 13.8 nL of 6-OHDA solution, injected at a rate of 23 nL/s at each site. The pipette was kept in place for 30 s after each injection and was then slowly removed. 6-OHDA solution was prepared using 11.76 mg 6-OHDA-HBr and 2 mg ascorbic acid in 1 mL of 0.9% normal saline solution. The solution was light-protected after preparation to prevent oxidation. In sham-operated birds, we performed the same surgical operations, except saline was injected into Area X instead of 6-OHDA. Again, this was consistent with methodology from prior studies (Hoffmann et al., 2016; Saravanan et al., 2019).

Birds recovered within 2 hr of surgery and began singing normally (at least 30 renditions of target syllable within 2 hr) typically 3–8 days after surgery. The number of songs sung per day did not differ significantly pre- vs. postlesion (paired t-test, p=0.290).

In order to confirm the effectiveness of 6-OHDA injections at lesioning dopaminergic input to Area X, we quantified the extent of the reduction of catecholaminergic fiber innervation within Area X after completing the behavioral experimentation in each bird (Hoffmann et al., 2016; Saravanan et al., 2019). To visualize dopaminergic innervation, we labeled tissue with a common biomarker for catecholaminergic cells (Figure 4—figure supplement 1a). To determine whether the concentration of dopaminergic fibers in Area X had decreased, we measured the optical density ratio (OD): the ratio of the stain density of Area X to the stain density of the surrounding striatum. OD ratios from individual 6-OHDA-lesioned brains decreased compared to control (Figure 4—figure supplement 1b). The distribution of all OD ratios from all of the 6-OHDA-lesioned tissue was significantly lower than that of the brain tissue from sham-operated birds (Figure 4—figure supplement 1c; two-sample Kolmogorov–Smirnov test, p<0.001). These results are similar to previous reports that used 6-OHDA injections to lesion dopaminergic input to Area X Hoffmann et al., 2016; Saravanan et al., 2019, and they indicate that the 6-OHDA injections successfully lesioned dopaminergic input to Area X.

Lesion size was quantified by determining the proportion of 6-OHDA-lesioned tissue that had an OD ratio of Area X to non-X striatum that was less than the fifth percentile of OD ratios in sham tissue. There was not a significant correlation between lesion size and the lesion-induced change in learning magnitude (post – pre) (Figure 4—figure supplement 2a and b; R² = 0.019, p=0.137).

Between 14 and 54 days after surgery, birds were injected with a lethal dose of ketamine and midazolam and were perfused. The tissue was post-fixed in 4% paraformaldehyde at room temperature for 4–16 hr and then moved to a solution of 30% sucrose for at least 1 day at 4°C for cryoprotection. Then, brain tissue was sliced in 40 μm sections. A chromogenic tyrosine hydroxylase (TH) stain was used to quantify the depletion of catecholaminergic fiber innervations in tissue collected from 6-OHDA-lesioned birds, and Nissl and fluorescent NeuN staining was used to assess the density of cell bodies in tissue from LMAN-lesioned and sham-operated birds. For one bird in the 6-OHDA-lesioned group, a Nissl stain was performed on alternate tissue sections to ensure no cell death occurred as a result of the lesion.

For TH immunohistochemistry, the tissue was incubated overnight in a primary anti-TH antibody solution. The tissue was next incubated in biotinylated horse anti-mouse secondary antibody solution for 1 hr. Then, the tissue was submerged in a diaminobenzidine (DAB) solution (two DAB tablets, Amresco E733 containing 5 mg DAB per tablet, 20 mL Barnstead H₂O, 3 μL H₂O₂) for less than 5 min for visualization. The DAB solution was prepared 1 hr prior to use. The tissue was washed, mounted, and coverslipped using Permount mounting medium.

Between each incubation, the tissue was washed with 0.1 M phosphate buffer (PBS) (23 g dibasic sodium phosphate, 5.25 g monobasic sodium phosphate, and 1 L deionized H₂O) three times for 10 min each. The tissue was first washed and then incubated in 0.3% H₂O₂ for 30 min and then 1% NaBH₄ for 20 min, followed by overnight incubation in a primary anti-TH antibody solution. The tissue was next incubated in biotinylated horse anti-mouse secondary antibody solution for 1 hr, then incubated in avidin-biotin-complex (ABC) solution for 1 hr that had been prepared 30 min prior to use. The tissue was then submerged in a DAB solution for less than 5 min. The tissue was then washed, mounted, and coverslipped using Permount mounting medium. These TH stains mark neurons expressing TH, which are catecholaminergic.

Tissue was washed in 0.1 M PBS three times for 10 min and was then mounted. The slides were incubated in Citrisolv twice for 5 min each, then delipidized in the following ethanol concentrations for 2 min each: 100, 100, 95, 95, and 70%. The tissue was briefly (less than 15 s) rinsed in deionized water, then incubated in cresyl violet (665 μL glacial acetic acid, 1 g cresyl violet acetate, and 200 mL deionized water) for 30 min. The tissue was rinsed in deionized water, then briefly (less than 15 s) submerged in the following ethanol concentrations for 2 min each: 70, 95, 95, 100, and 100%. The tissue was then incubated in Citrisolv twice for 5 min. The tissue was coverslipped using Permount mounting medium. These Nissl stains mark neuronal cell bodies.

Between each incubation, the tissue was washed with 0.1 M PBS three times for 10 min each. The tissue was incubated in primary antibody solution (4 mL EMD Millipore guinea pig anti-NeuN Alexa Fluor 488 antibody, 6 mL Triton X-100, 20 mL normal donkey serum [NDS], and 1.95 mL 0.1 M PBS) overnight. The tissue was then washed and incubated in a secondary antibody solution (10 mL Jackson Labs donkey anti-guinea pig [DAG], 6 mL Triton X-100, and 1.975 mL 0.1 PBS) overnight. The tissue was then washed, mounted, and coverslipped with FluroGel mounting medium. Slides were sealed with lacquer. Images were taken under a widefield microscope (BioTek Lionheart FX, Sony ICX285 CCD camera, Gen5 acquisition software, ×1.25 magnification, 16-bit grayscale).

Analysis of lesions was based on previously published methodology (Hoffmann et al., 2016; Saravanan et al., 2019). Images of stained tissue sections were obtained using a slide scanner and were converted into 8-bit grayscale images in ImageJ. In birds that received sham 6-OHDA lesions, Area X stains darker than surrounding striatum in TH-DAB-stained tissue due to a higher density of catecholaminergic inputs in Area X Hoffmann et al., 2016; Saravanan et al., 2019. The baseline level of stain darkness can vary from bird to bird. Therefore, rather than directly comparing the stain density of lesioned and sham tissue, the ratio of the stain density of Area X to that of the surrounding striatum (OD ratio) was calculated to determine whether the concentration of catecholaminergic fibers was decreased. Prior work demonstrated that the vast majority of catecholaminergic input to Area X is dopaminergic (Hoffmann et al., 2016).

For each section of tissue containing Area X, a customized ImageJ macro was used to select regions of interest (ROIs) within Area X and within a portion of striatum outside Area X by manually outlining Area X and selecting a circular 0.5-mm-diameter region of striatum anterior to Area X. Pixel count and OD of each ROI were measured, and the density of TH-positive fibers was calculated using the ratio of the OD of Area X to the OD of non-X-striatum.

The cumulative distribution of OD ratios for sham-operated birds was used to construct a 95% confidence interval and determine the threshold for lesioned tissue. 6-OHDA-lesioned tissue in which the OD ratio fell below the 5th percentile of control tissue had a significantly reduced TH-positive fiber density.

All error bars presented in the article represent SEM. When assessing whether a significant amount of vocal learning occurred in one experiment, we used one-sample t-tests to compare the mean pitch on the final day of training vs zero. To assess whether a significant difference in amount of learning occurred within an individual bird pre- vs. postlesion, we used paired t-tests. To assess significance between distributions of target syllable pitches on various days of the experiment (baseline, cutaneous stimulation training, washout), we used a two-sample Kolmogorov–Smirnov test.

Each experimental group had at least five birds, and for each bird, the target syllable was typically repeated well over 30 times a day. Therefore, the structure of our data is hierarchical, so error accumulates at different levels (birds and syllable iterations). Simply grouping all the data together ignores the non-independence between samples and underestimates the error. To address this issue, we employed a hierarchical bootstrap method to measure SEM and calculate p-values (Saravanan et al., 2020). For each experimental day, we calculated normalized pitch values (in semitones) (normalized to the mean pitch on the final baseline day during that particular experiment). We then generated a population of 10,000 bootstrapped means according to the following sampling procedure: to generate each individual subsample, we resampled across each level of hierarchy in our data (first resampled among the birds, then for each selected bird, we resampled among syllable iterations). The standard deviation of this population of bootstrapped means provides an accurate estimate of the uncertainty of the original data (Saravanan et al., 2020; Saravanan et al., 2019). Thus, the SEM values (which are used for error bars) we report when employing the hierarchical bootstrap method are equal to this standard deviation.

To calculate p-values and determine significance for comparing our data to zero using the hierarchical bootstrap method, we calculated P_boot: the proportion of bootstrapped means greater than zero compared to the total number of bootstrapped means. Using an acceptable type 1 error rate of 0.05, any value of this P_boot ratio greater than 0.975 indicates the mean was significantly greater than zero and any value less than 0.025 indicates the mean was significantly less than zero. P_boot values between 0.025 and 0.975 indicate no significant difference between the dataset and zero. Because we measure adaptive pitch changes in semitones, which are a normalized measure of pitch change where baseline is set to zero, this method of calculating P_boot was employed in all instances where it was necessary to assess whether there was a significant change in pitch at the end of training compared to baseline (zero).

We also sometimes sought to determine significance for the comparison of two means rather than what was previously described (where we assess significance between one mean compared to baseline [zero]). We used a similar hierarchical bootstrap statistical methodology and calculated P_boot. The key difference is that, rather than measuring the proportion of resampled means greater than or less than zero, we instead calculate a joint probability distribution for the means of the two resampled datasets. We measured the percentage of this joint probability distribution that was above one side of the unity line. This percentage is the P_boot value we report in these instances. If the proportion of this joint probability distribution that falls above the unity line is greater than 0.975, it indicated a significantly greater mean of dataset 1 over dataset 2. If the percentage of the joint probability distribution that was above the unity line was less than 0.025, it indicated a significantly lower mean of dataset 1 compared to dataset 2. P_boot values between 0.025 and 0.975 indicate no significant difference between the two datasets. This method was employed in all instances where it was necessary to assess whether the learning magnitudes (adaptive pitch changes by the end of training) were significantly different pre- vs. postlesion (or pre- vs. postsham) or across experimental conditions (e.g., postsham vs. postlesion or post-LMAN lesion vs. post-6-OHDA lesion).

In both forms of P_boot calculation, the lowest statistical limit for P_boot is P_boot < 0.0010, due to resampling 10⁴ times to create bootstrapped means. The highest possible limit for P_boot is P_boot > 0.9999, for the same reason.

Sample sizes were not predetermined using a power analysis. Sample sizes of all sets of experiments were comparable to relevant prior literature (Hoffmann et al., 2016; Tumer and Brainard, 2007; Saravanan et al., 2019). If at any point during cutaneous stimulation training or white noise training a bird’s rate of singing dropped below 10 songs per day for over 1 day, that experiment was stopped and the data were excluded from further analysis.

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

This work was supported in part by NIH grants R01-EB022872, R01-NS084844, and R01-NS099375, a grant from the Simons Foundation as part of the Simons-Emory International Consortium on Motor Control, and HHMI.

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

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)

© 2022, McGregor, Grassler et al.

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