article: What cortical inputs are provided to motor control areas while they drive complex learned behaviors? We study this question in the nucleus interface of the nidopallium (NIf), which is required for normal birdsong production and provides the main source of auditory input to HVC, the driver of adult song. In juvenile and adult zebra finches, we find that spikes in NIf projection neurons precede vocalizations by several tens of milliseconds and are insensitive to distortions of auditory feedback. We identify a local isometry between NIf output and vocalizations: quasi-identical notes produced in different syllables are preceded by highly similar NIf spike patterns. NIf multiunit firing during song precedes responses in auditory cortical neurons by about 50 ms, revealing delayed congruence between NIf spiking and a neural representation of auditory feedback. Our findings suggest that NIf codes for imminent acoustic events within vocal performance.
  Some highly skilled learned behaviors, such as speech or birdsong, are encoded primarily in dedicated cortical brain areas [1–4]. To assure integration with other behaviors and with the sensory environment, these cortical pattern generators must rely on inputs that convey information about motor plans, desired sensory targets, and sensory feedback [3,5, 6]. However, the integration of motor signals with sensory, planning, and decision-related signals remains highly enigmatic. Songbirds are ideally suited to decipher neural integration in skilled behaviors because they learn their courtship songs by hearing and memorizing a song template from a tutor and by adjusting their immature songs using auditory feedback [7–10]. Although calling in birds per se does not require cortical motor input [1], analogous to whisking in rodents [11], the highly stereotyped songs of adult birds are driven by the dedicated cortical area HVC [1,12], whose projection neurons fire ultra-sparsely in time [13,14]. Here we investigate HVC’s input from the cortical nucleus interface of the nidopallium (NIf). NIf is a thin plate of cells encapsulated between field L areas, from which NIf indirectly receives auditory input [15] and relays it to HVC [16–20]. NIf activity is required for song learning [6] as well as for normal song production [21,22], although song recovers within a few days after irreversible NIf lesions [23]. NIf neural activity increases during song production [24,25], but the motor and auditory-related firing patterns in NIf neurons that project to HVC (NIfHVC neurons) have never been investigated in awake, singing birds. We find that during song production, NIf neurons do not respond to auditory white noise stimuli, making it very unlikely that NIf relays auditory feedback to HVC. NIfHVC spikes tend to precede song syllables, spike patterns are highly syllable-specific, and they occur in distinguished and stereotyped patterns prior to individual song notes. Highly similar notes in different song syllables are preceded by highly similar NIfHVC spike patterns, revealing a locally isometric relationship between NIf output and vocal output. Multi-unit activity in NIf during singing is correlated with subsequent activity in field L, pointing at a neural code in NIf that anticipates auditory feedback.
  We found that NIfHVC neurons were spontaneously active during quiescent non-singing periods and increased their firing rates before song onsets (Fig 1A–1C). In two juvenile birds that started their songs with either one of several types of introductory notes or calls, NIfHVC neurons fired highly distinct spike patterns prior to these different onsets, ranging from almost no spikes prior to some note/call onsets, up to a high-frequency burst of more than four spikes prior to other onsets, revealing differential coding of song onset type. On average, NIfHVC firing rates gradually increased before song onset and significantly deviated from pre-song baseline firing (greater than 3 jackknife standard deviations) already 70 ms prior to song onset (Fig 1C). The firing rate during song of 37 Hz (averaged from -70 to +160 ms of song onsets) was much higher than the 8 Hz baseline firing rate (averaged from -300 to -100 ms before song onset, p = 0. 0005 Wilcoxon signed rank test, n = 16 NIfHVC cells, excluding four cells in which we did not record sufficient amounts of baseline activity prior to song onsets). NIfHVC firing also returned to baseline prior to song offsets (Fig 1D). On average, NIfHVC firing rates were indistinguishable from post-song baseline firing (less than three jackknife standard deviations, n = 16 cells) already 34 ms prior to song offset (Fig 1E). Thus, HVC receives excessive NIf input long before song onset, and this input wanes before song offset. Both single and multi-unit activity displayed rhythmic and stereotyped spike patterns during song motifs (Fig 2A). NIfHVC neurons fired high-frequency bursts (range 100–800 Hz) that were time-locked to individual song syllables. To estimate NIfHVC burst precision, we analyzed the onset time jitter of a set of 10 well-isolated bursts (n = 6 NIfHVC neurons) relative to syllable onset and found root-mean square (RMS) burst-onset jitters in juvenile birds of 2. 92 ± 0. 76 ms (std, n = 12 NIfHVC neurons). Burst onsets in adults were slightly more precise than in juveniles (RMS burst onset jitter 1. 92 ± 0. 52 ms [std, n = 8 NIfHVC neurons]), though the jitter difference did not reach significance (p = 0. 06). We estimated the time lag at which syllable onsets and offsets (Fig 2B) followed NIfHVC spikes by calculating cross-covariance (CC) functions between NIfHVC spikes and syllable onset/offset events (Fig 2C). On average, NIfHVC activity peaked 20 ms before syllable onsets, and it dipped 20 ms before syllable offsets. A clear lead of NIfHVC spikes on song was also evident in CC functions between NIfHVC spike trains and sound amplitudes. Averaged over all 20 cells, sound amplitudes peaked 46 ms after NIfHVC spikes, though individual cells displayed a broad range of peak latencies from 7 to 61 ms (median latency = 42 ms; Fig 2D). The broadly distributed spike-to-sound latencies suggest that NIfHVC cells fire at short but mostly nonzero time lags of each other. A spike-spike cross-correlation analysis of (serially recorded) NIfHVC neurons confirmed this trend: Cross-correlation functions of song-related spike trains peaked at broadly distributed time lags in the range 0 to 300 ms (median lag 70 ms, n = 8 NIfHVC neuron pairs in two birds). Thus, despite their common property of leading vocal sound amplitudes, we found no evidence for strongly synchronized activity among NIfHVC neurons. NIfHVC cells did not only fire spike bursts prior to song and syllable onsets, but also frequently close to the middle of syllables including harmonic stacks, illustrated in Fig 2A. To test the model that NIfHVC spikes tend to cause transient increases in sound amplitudes at a time lag unique to each cell, we computed the sensitivity index (or d′) of spike-triggered sound amplitudes at the preferred time lag for each cell (i. e. , the time lag of peak spike-triggered sound amplitude). The sensitivity index measures the separation between the means of the signal (sound amplitudes at preferred time lag) and the noise distribution (amplitudes at spike time), in units of signal and noise standard deviations. Measured d′ values across the population of cells were very small (median d′ = 0. 15, mean d′ = 0. 29, range 0. 04–1. 2, n = 20 cells), implying that a temporally locked spike-sound model provides a poor account of NIfHVC firing behavior, despite the consistent peak of song amplitudes 50 ms after NIfHVC spikes (we obtained similar conclusions when using Student’s t test to compare sound amplitudes at spike and peak times, p = 0. 12). Because songbirds use auditory feedback to learn their songs, it is crucial to understand in which cell populations this feedback is processed and conveyed to motor areas such as HVC. In HVC projection neurons, there are no traces of auditory feedback responses during singing, not even subthreshold [26–28]. One possibility is that NIf is responsive to auditory feedback and that these responses are gated off in NIf-HVC synapses. To test for this possibility, we perturbed birds during song with an auditory stimulus (white noise or zebra finch call) that we time-locked to a randomly chosen syllable using real-time note detection. We produced raster plots of NIf spikes aligned with song motifs. All NIf neurons tested in juveniles and adults (in four adults: n = 2 putative NIf interneurons, n = 2 NIfHVC neurons, and n = 2 multi-unit sites; in four juveniles: n = 3 NIfHVC neurons, n = 1 putative NIf interneuron, and n = 7 multi-unit sites) displayed indistinguishable firing during perturbed and unperturbed songs, even when perturbations were delivered at amplitudes up to 6 dB above maximal song amplitudes (recorded at the microphone roughly 20 cm away from the bird; Fig 3). Given these data, it is very unlikely that NIf neurons in either juveniles or adults convey song-related auditory feedback to HVC. We found no evidence that NIf neurons signal expected auditory feedback such as could arise, for example, from an efference copy of HVC motor commands relayed to NIf via nucleus avalanche (Av) [29]. Three birds (one adult and two juveniles) truncated their song motifs by occasionally dropping the last syllable. In these birds, during renditions of the non-truncated motif, all neurons and multi-unit sites fired at least one spike within a 50 ms window preceding the onset of the last syllable (n = 4 NIfHVC neurons and n = 4 multi-unit sites). By contrast, in renditions of truncated motifs, all neurons remained virtually silent in the same 50 ms window (the example cell shown in Fig 4A dropped two bursts in truncated motifs, revealing that behavioral units such as song syllables can be associated with extended sequences of spike trains containing more than a single burst). Based on these truncations, we conclude that NIf activity at the end of the second-to-last syllable was linked to the last syllable but not to expected feedback from the second-to-last syllable. Indeed, in one bird in which perturbations induced the bird to truncate its song, we observed cessation of NIf multi-unit activity, at the earliest, less than 50 ms after perturbation onset (Fig 4B), which is earlier than the median delay of perturbation responses in field L and caudolateral mesopallium (CLM) [5]. The precise stopping time of NIf activity was highly predictive of the ensuing song truncation point, revealing that short-latency NIf firing differences to distorted auditory feedback constitute premotor activity rather than a pure sensory response, because they reflect a sensory-induced change in motor plan. These findings corroborate the notion that rather than being post-vocal (e. g. , expected auditory feedback of past vocalizations), NIfHVC activity during singing is pre-vocal (leading vocalizations, possibly including a plan of desired auditory feedback). To assess NIfHVC firing stereotypy relative to brief recurring song notes, we applied a method of birdsong analysis based on efficient coding ideas [30]. For each bird and day of recording, we computed a set of 100 linear filters forming a sparse representation of songs. This representation captured, on average, 94. 4% of the song spectrogram variance (range 91% to 98. 4% across birds and days). Each filter, when applied to song spectrograms, formed a detector of either a particular song note or variants of a note (see Materials and Methods). When we time-aligned NIfHVC spike patterns with individually detected notes, we found that 80 ms spike patterns preceding notes were very stereotyped; in fact, more stereotyped than patterns following notes (median Fpre = 24. 3 > median Fpost = 16. 8, sign rank test, p < 5 × 10−5, n = 20 cells). Thus, NIfHVC spike sequences were better aligned with future notes than with past notes. We verified that no filter chosen in the analysis formed a syllable onset detector (S1 Fig), to rule out replication of our previous syllable onset analysis. NIfHVC firing rates did not correlate with subtle variability of syllables and notes across motif renditions (excluding tempo). In spike raster plots, we selected time windows that contained a single high-frequency burst in most motif renditions and correlated the number of spikes within these windows with the magnitude of the associated note (defined as the maximal filter response within 0 to 80 ms after burst onset). Despite running this analysis for each of the 100 filters and on all 20 NIfHVC cells, we found significant correlations between spike counts and note strength in only 3 of 60 analyzed bursts. The few significant correlations we observed were all seen for sub-notes (a variant of a note that is produced in roughly 50% of motifs; see Materials and Methods and also [30]). Hence, given this predominant lack of correlation between the magnitude of NIfHVC bursts and note strength, we conclude that subtle NIfHVC firing variability is not simply related with subtle variability in produced sound features. NIfHVC spike sequences displayed an isometric relationship with song notes, meaning that preceding similar notes, the spike sequences were similar as well. In some birds, we spotted extended vocal sequences that seemed to repeat. The juvenile song motif shown in Fig 4A contained a quasi-repetition of a sequence of two syllables (“quasi-repetition” because repeated syllables were similar but strictly non-identical; see Materials and Methods). Interestingly, spike sequences in all four NIfHVC neurons recorded during this quasi-repetition also formed a quasi-repetition. Hence, NIfHVC population activity can be similar when preceding similar vocal outputs. To quantify these isometric firing tendencies, we focused our attention on repeated notes. Some birds produced a particular note more than once within single song motifs (n = 4 birds). Closer inspection revealed that such repeated notes were highly similar but non-identical (“quasi-identical, ” see Materials and Methods). To examine similarity of NIfHVC spike trains preceding a pair of quasi-identical notes, we computed the correlation coefficient among binned NIfHVC spike patterns (n = 16 NIfHVC cells) restricted to 80 ms before each note (Fig 5). Correlation coefficients were much higher when the spike patterns preceded quasi-identical notes than when they followed quasi-identical notes (median rpre = 0. 71 > median rpost = 0. 06, p < 2 × 10−7, n = 47 notes, Wilcoxon rank-sum test; Fig 6). Also, spike trains preceding quasi-identical notes were more similar than spike trains preceding non-similar notes (median rrandom = –0. 15, p < 2 × 10−15, Wilcoxon rank-sum test; Figs 5 and 6). Thus, NIfHVC activity reflected a high similarity of ensuing vocalizations by producing highly similar spike patterns. On average, firing rates in 80 ms windows were higher preceding notes than after notes (fpre = 37. 5 Hz, fpost = 30. 7 Hz). However, these differences could not explain the higher similarity of NIfHVC firing prior to quasi-identical notes: After randomly removing spikes in pre-note windows to equalize average firing rates in pre- and post-note windows, the similarity among NIfHVC spike trains preceding quasi-identical notes was still exceedingly high (median rpre = 0. 66 > median rpost = 0. 07, p < 10−6; median rrandom = –0. 15, p < 2 × 10−14, Wilcoxon rank-sum test). We hypothesized that there is a simple predictive relationship between NIf’s motor code and the neural representation of auditory feedback in field L. To directly compare NIf’s motor code with field L’s feedback responses, we recorded multi-unit activity during singing simultaneously in NIf and in the surrounding lateral field L using a 16-pad linear electrode array (silicon probe, see Materials and Methods). Electrodes were implanted such that the tip of the electrode array was located in field L2a, the middle in NIf, and the top recording pads picked up signals from field L1 (Fig 7). Multi-unit firing during singing increased markedly in L2a and in NIf but only weakly in L1 (Fig 7A and 7B). Playback of a loud white-noise stimulus during singing (song perturbation) caused the bird to truncate its song; but unlike in NIf, neurons in field L1 and L2a responded strongly to such white-noise stimuli, thus demonstrating that in field L we recorded feedback-sensitive neurons [5]. A CC analysis between NIf and L2a activity during unperturbed singing showed that NIf firing did consistently lead L2a responses by several tens of milliseconds. NIf-L2a CC functions peaked significantly (more than three standard deviations above zero correlation, p < 0. 01) at lags within the range 42–58 ms after NIf spikes (n = 33 pad pairs in NIf and L2a from 36 possible pairs, n = 3 birds, Fig 7C). Additionally, the CC at a 50-ms lag was significant in 28/36 NIf-L2a pairings examined. Interestingly, in all cases, the CC peaks also exceeded by, on average, 18% a shuffle predictor defined by cross-correlating NIf activity during song rendition i with field L2a activity during song rendition i + 1 (circular boundary condition). Hence, small variability in NIf firing was followed by aligned variability in field L2a, suggesting that NIf firing encodes a motor plan in a representation that is matched by sound sensitivity in field L2a. To inspect the fraction of field L’s song-related response variance predicted by NIf activity, we modeled the mean-subtracted firing rate fL (t) in field L2a as a constant times the mean-subtracted firing rate fN (t) in NIf 50 ms earlier: fL (t) = cfN (t − 50 ms), with c being a multiplicative constant we determined for each recording site on training data of paired field L2a–NIf recordings during singing. We found that this simple linear regression model with a single scalar parameter (the constant c) accounts, on average, for about 20% of firing variability in field L (on test data excluded during training). Thus, on the coarse level of multi-unit activity, the pre-vocal code in NIf was predictive of a neural representation of auditory feedback.
  We showed that NIf firing during song is motor-related and that it encodes neither actual nor expected auditory feedback of past vocalizations. First, distortions of auditory feedback during singing did not elicit differential NIfHVC responses that have been shown to exist in field L [5] and other higher auditory areas [31]. Second, the firing stereotypy of NIfHVC neurons was higher before song notes than after notes, supporting pre-vocal (predictive) rather than post-vocal (postdictive) coding. Third, locally, we found an interesting anticipatory congruence between NIfHVC spikes and vocalizations, manifest as a preservation of similarities between spike patterns and subsequent song features. Namely, in 60–100 ms windows preceding quasi-identical notes, mutual similarity of NIfHVC firing patterns was much higher than expected by chance, even after discounting for biases in mean firing rates. Thus, NIfHVC firing is isometrically linked and strongly constrained by future vocalizations. Fourth, we found NIfHVC activity to lead by several tens of milliseconds auditory activity in surrounding field L. Together, these findings suggest that NIf has a role in motor planning of sensory targets, in alignment with the reduced complexity of Bengalese finch songs following NIf lesions [32]. The vocal lead by 45–50 ms of NIf premotor activity was larger than observed latencies in the downstream motor pathway. For example, the time lag between the robust nucleus of the arcopallium (RA) premotor activity and sound production is on the order of 15–20 ms [33,34]. Combined with an antidromic latency from RA to HVC of about 10 ms [13], we obtain an expected latency from HVC to vocal output of roughly 25–30 ms. Indeed, a recent study found a total latency from HVC to motor output of about 29 ms for isolated projecting cells [35], which is considerably shorter than our measured NIfHVC latencies. The isometric NIf code might be generated locally, or it could rely critically on communication with other sensori-motor areas including HVC, field L, the caudal mesopallium (CM) [16,20] with its nucleus avalanche (Av) [29], the caudomedial nidopallium (NCM) [36], and nucleus Uvaformis (Uva) [37]. Among these areas, we believe Av to be particularly important due its shared role (with NIf) of driving HVC auditory responses [19]. NIf’s pre-vocal coding of auditory features might be very complex. Namely, across NIfHVC populations, we found no simple relationship between spike rates and vocal output: there were no excessive correlations between spike counts in NIfHVC bursts and the feature composition of song notes. One possibility is that NIf does not code for note variants, another is that the NIf note code might not rely on spike counts. In support of this view, recent studies have shown that in a song pre-motor nucleus, the RA, spike rates are less informative of song features than precise spike-timing codes [38–41]. The role of NIf for singing is comparable with the role of RA for calling. NIf fires in anticipatory manners prior to songs and song syllables, but reversible NIf lesions do not eliminate song [23], just like RA that fires prior to calls [42] but is not required for production of some calls [43]. It is possible that these analogies reflect an evolutionary strategy of linking vocal control areas (HVC, respectively, the dorsomedial nucleus, DM) with upstream planning areas that dynamically trigger behavioral modules but that are not required for their execution. What might be the origin of NIf' s local firing isometry? We speculate that a local isometry could be a remnant of a vocal learning strategy based on duplicating an ancestor syllable and individually reshaping its copies into their corresponding targets [44–46]. NIf firing prior to quasi-identical notes may reflect the motor signal of the common ancestor syllable. If true, the NIf code we discovered might constrain the space within which songs can develop by limiting acoustic deviations from ancestor notes. How does our interpretation of the isometric NIf code relate to motor control concepts? Our findings agree with the notion that NIf acts as a feedforward controller driving motor gestures encoded in HVC and allows birds to incorporate desired acoustic features into their songs [47–49]. In engineering terms, a link from desired auditory features (in NIf) onto corresponding motor commands (in HVC) is known as an inverse model. The synaptic connections from NIf to HVC could constitute a mapping of desired vocal output onto the motor commands causally required to generate that output, implying that an inverse song model could be stored in the synaptic patterns linking NIf with HVC. Possibly, nidopallial vocal-control areas might share a common strategy of computing inverse models [50]. Premotor codes that are informative about expected sensory feedback are abundant. For example, to support visually guided reaching arm movements, neurons in many premotor brain areas of monkeys have eye-centered reach fields; neurons in regions of posterior parietal cortex and in the medial intraparietal area respond when monkeys prepare to reach for a target within the reach field [51–54]. It was argued that one benefit of these eye-centered coordinates is to allow for simple communication of spatial information among parietal lobe areas. For similar reasons, such communication strategies would also be beneficial to vocal control.
  All procedures described here were approved by the Veterinary Office of the Canton of Zurich, Switzerland. Male zebra finches (Taeniopygia guttata castanotis), ranging from 28 to 400 days of age, were obtained from our breeding colony or from a local supplier. Adults and juvenile birds used were raised with their parents and siblings. Two to three days before surgery, the birds were placed in a cage inside the recording chamber for song recording. In case of chronic neuronal recordings, birds were kept there for the remainder of the experiment. Except for three neurons in one adult bird, all recordings in freely behaving birds were made in an undirected setting (no other bird present in the chamber). Electrophysiological signals were amplified, filtered, and sampled at 32 kHz.
summary: Transmission of birdsong across generations requires tight interactions between auditory and vocal systems. However, how these interactions take place is poorly understood. We studied neuronal activity in the brain area located at the intersection between auditory and song motor areas, which is known as the nucleus interface of the nidopallium. By recording during singing from neurons in the nucleus interface of the nidopallium that project to motor areas, we found that their spiking precedes peaks in vocal amplitudes by about 50 ms. Notably, quasi-identical notes produced at different times in the song motif were preceded by highly similar spike patterns in these projection neurons. Such local isometry between output from the nucleus interface of the nidopallium and vocalizations suggests that projection neurons in this brain area code for imminent acoustic events within vocal performance. In support of this conclusion, during singing, projection neurons do not respond to playback of white noise sound stimuli, and activity in the nucleus interface of the nidopallium precedes by about 50 ms neural activity in the avian analogue of auditory cortex. Therefore, we conclude that the role of neuronal activity in the nucleus interface of the nidopallium could be to link desired auditory targets to suitable motor commands required for hitting these targets.
keywords: syllables
  sequencing techniques
  ornithology
  linguistics
  vocalization
  vertebrates
  social sciences
  neuroscience
  animals
  animal signaling and communication
  motor neurons
  animal behavior
  bird song
  molecular biology techniques
  sequence motif analysis
  zoology
  research and analysis methods
  sequence analysis
  computer and information sciences
  birds
  animal cells
  behavior
  molecular biology
  cellular neuroscience
  raster plots
  cell biology
  data visualization
  neurons
  phonology
  biology and life sciences
  cellular types
  amniotes
  organisms
title: A Neural Code That Is Isometric to Vocal Output and Correlates with Its Sensory Consequences