Growth and splitting of neural sequences in songbird vocal development

SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach

The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs.

Note similarities affect syntactic stability in zebra finches

The acquisition of an acoustic template is a fundamental component of vocal imitation learning, which is used to refine innate vocalizations and develop a species-specific song. In the absence of a model, birds fail to develop species typical songs. In zebra finches (Taeniopygia guttata), tutored birds produce songs with a stereotyped sequence of distinct acoustic elements, or notes, which form the song motif. Songs of untutored individuals feature atypical acoustic and temporal structure. Here we studied songs and associated respiratory patterns of tutored and untutored male zebra finches to investigate whether similar acoustic notes influence the sequence of song elements. A subgroup of animals developed songs with multiple acoustically similar notes that are produced with alike respiratory motor gestures. These birds also showed increased syntactic variability in their adult motif. Sequence variability tended to occur near song elements which showed high similarity in acoustic structure and underlying respiratory motor gestures. The duration and depth of the inspirations preceding the syllables where syntactic variation occurred did not allow prediction of the following sequence of notes, suggesting that the varying duration and air requirement of the following expiratory pulse is not predictively encoded in the motor program. This study provides a novel method for calculation of motor/acoustic similarity, and the results of this study suggest that the note is a fundamental acoustic unit in the organization of the motif and could play a role in the neural code for song syntax.

Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA.

Tutor auditory memory for guiding sensorimotor learning in birdsong

Memory-guided motor shaping is necessary for sensorimotor learning. Vocal learning, such as speech development in human babies and song learning in bird juveniles, begins with the formation of an auditory template by hearing adult voices followed by vocally matching to the memorized template using auditory feedback. In zebra finches, the widely used songbird model system, only males develop individually unique stereotyped songs. The production of normal songs relies on auditory experience of tutor's songs (commonly their father's songs) during a critical period in development that consists of orchestrated auditory and sensorimotor phases. "Auditory templates" of tutor songs are thought to form in the brain to guide later vocal learning, while formation of "motor templates" of own song has been suggested to be necessary for the maintenance of stereotyped adult songs. Where these templates are formed in the brain and how they interact with other brain areas to guide song learning, presumably with template-matching error correction, remains to be clarified. Here, we review and discuss studies on auditory and motor templates in the avian brain. We suggest that distinct auditory and motor template systems exist that switch their functions during development.

Dimensionality reduction beyond neural subspaces with slice tensor component analysis

Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.

Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation

Working memory (WM) is the ability to maintain and manipulate information 'in mind'. The neural codes underlying WM have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of male macaque monkeys during a visuospatial WM task that required navigation in a virtual 3D environment. Here, we demonstrate distinct neuronal activation sequences (NASs) that encode remembered target locations in the virtual environment. This NAS code outperformed the persistent firing code for remembered locations during the virtual reality task, but not during a classical WM task using stationary stimuli and constraining eye movements. Finally, blocking NMDA receptors using low doses of ketamine deteriorated the NAS code and behavioral performance selectively during the WM task. These results reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.

A predisposed motor bias shapes individuality in vocal learning

The development of individuality during learned behavior is a common trait observed across animal species; however, the underlying biological mechanisms remain understood. Similar to human speech, songbirds develop individually unique songs with species-specific traits through vocal learning. In this study, we investigate the developmental and molecular mechanisms underlying individuality in vocal learning by utilizing F1 hybrid songbirds (Taeniopygia guttata cross with Taeniopygia bichenovii), taking an integrating approach combining experimentally controlled systematic song tutoring, unbiased discriminant analysis of song features, and single-cell transcriptomics. When tutoring with songs from both parental species, F1 hybrid individuals exhibit evident diversity in their acquired songs. Approximately 30% of F1 hybrids selectively learn either song of the two parental species, while others develop merged songs that combine traits from both species. Vocal acoustic biases during vocal babbling initially appear as individual differences in songs among F1 juveniles and are maintained through the sensitive period of song vocal learning. These vocal acoustic biases emerge independently of the initial auditory experience of hearing the biological father's and passive tutored songs. We identify individual differences in transcriptional signatures in a subset of cell types, including the glutamatergic neurons projecting from the cortical vocal output nucleus to the hypoglossal nuclei, which are associated with variations of vocal acoustic features. These findings suggest that a genetically predisposed vocal motor bias serves as the initial origin of individual variation in vocal learning, influencing learning constraints and preferences.

Neural Sequences and the Encoding of Time

Converging experimental and computational evidence indicate that on the scale of seconds the brain encodes time through changing patterns of neural activity. Experimentally, two general forms of neural dynamic regimes that can encode time have been observed: neural population clocks and ramping activity. Neural population clocks provide a high-dimensional code to generate complex spatiotemporal output patterns, in which each neuron exhibits a nonlinear temporal profile. A prototypical example of neural population clocks are neural sequences, which have been observed across species, brain areas, and behavioral paradigms. Additionally, neural sequences emerge in artificial neural networks trained to solve time-dependent tasks. Here, we examine the role of neural sequences in the encoding of time, and how they may emerge in a biologically plausible manner. We conclude that neural sequences may represent a canonical computational regime to perform temporal computations.

Algebraic approach to spike-time neural codes in the hippocampus

Although temporal coding through spike-time patterns has long been of interest in neuroscience, the specific structures that could be useful for spike-time codes remain highly unclear. Here, we introduce an analytical approach, using techniques from discrete mathematics, to study spike-time codes. As an initial example, we focus on the phenomenon of "phase precession" in the rodent hippocampus. During navigation and learning on a physical track, specific cells in a rodent's brain form a highly structured pattern relative to the oscillation of population activity in this region. Studies of phase precession largely focus on its role in precisely ordering spike times for synaptic plasticity, as the role of phase precession in memory formation is well established. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for a function mapping points in physical space to complex-valued spikes by representing individual spike times as complex numbers. The properties of this function make explicit a specific relationship between past and future in spike patterns of the hippocampus. Importantly, this mathematical approach generalizes beyond the specific phenomenon studied here, providing a technique to study the neural codes within precise spike-time sequences found during sensory coding and motor behavior. We then introduce a spike-based decoding algorithm, based on this function, that successfully decodes a simulated animal's trajectory using only the animal's initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average firing rate. These results illustrate the utility of a discrete approach, based on the structure and symmetries in spike patterns across finite sets of cells, to provide insight into the structure and function of neural systems.

Self-organization of songbird neural sequences during social isolation

Behaviors emerge via a combination of experience and innate predispositions. As the brain matures, it undergoes major changes in cellular, network, and functional properties that can be due to sensory experience as well as developmental processes. In normal birdsong learning, neural sequences emerge to control song syllables learned from a tutor. Here, we disambiguate the role of tutor experience and development in neural sequence formation by delaying exposure to a tutor. Using functional calcium imaging, we observe neural sequences in the absence of tutoring, demonstrating that tutor experience is not necessary for the formation of sequences. However, after exposure to a tutor, pre-existing sequences can become tightly associated with new song syllables. Since we delayed tutoring, only half our birds learned new syllables following tutor exposure. The birds that failed to learn were the birds in which pre-tutoring neural sequences were most 'crystallized,' that is, already tightly associated with their (untutored) song.

Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance

Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.

Thalamus drives vocal onsets in the zebra finch courtship song

While motor cortical circuits contain information related to specific movement parameters1, long-range inputs also have a critical role in action execution2,3. Thalamic projections can shape premotor activity2-6 and have been suggested7 to mediate the selection of short, stereotyped actions comprising more complex behaviours8. However, the mechanisms by which thalamus interacts with motor cortical circuits to execute such movement sequences remain unknown. Here we find that thalamic drive engages a specific subpopulation of premotor neurons within the zebra finch song nucleus HVC (proper name) and that these inputs are critical for the progression between vocal motor elements (that is, 'syllables'). In vivo two-photon imaging of thalamic axons in HVC showed robust song-related activity, and online perturbations of thalamic function caused song to be truncated at syllable boundaries. We used thalamic stimulation to identify a sparse set of thalamically driven neurons within HVC, representing ~15% of the premotor neurons within that network. Unexpectedly, this population of putative thalamorecipient neurons is robustly active immediately preceding syllable onset, leading to the possibility that thalamic input can initiate individual song components through selectively targeting these 'starter cells'. Our findings highlight the motor thalamus as a director of cortical dynamics in the context of an ethologically relevant behavioural sequence.

Rhythmically bursting songbird vocomotor neurons are organized into multiple sequences, suggesting a network/intrinsic properties model encoding song and error, not time

In zebra finch, basal ganglia projecting "HVC _X " neurons emit one or more spike bursts during each song motif (canonical sequence of syllables), which are thought to be driven in part by a process of spike rebound excitation. Zebra finch songs are highly stereotyped and recent results indicate that the intrinsic properties of HVC _X neurons are similar within each bird, vary among birds depending on similarity of the songs, and vary with song errors. We tested the hypothesis that the timing of spike bursts during singing also evince individual-specific distributions. Examining previously published data, we demonstrated that the intervals between bursts of multibursting HVC _X are similar for neurons within each bird, in many cases highly clustered at distinct peaks, with the patterns varying among birds. The fixed delay between bursts and different times when neurons are first recruited in the song yields precisely timed multiple sequences of bursts throughout the song, not the previously envisioned single sequence of bursts treated as events having statistically independent timing. A given moment in time engages multiple sequences and both single bursting and multibursting HVC _X simultaneously. This suggests a model where a population of HVC _X sharing common intrinsic properties driving spike rebound excitation influence the timing of a given HVC _X burst through lateral inhibitory interactions. Perturbations in burst timing, representing error, could propagate in time. Our results extend the concept of central pattern generators to complex vertebrate vocal learning and suggest that network activity (timing of inhibition) and HVC _X intrinsic properties become coordinated during developmental birdsong learning.

Scalable Three-Dimensional Recording Electrodes for Probing Biological Tissues

Electrophysiological recording technologies can provide critical insight into the function of the nervous system and other biological tissues. Standard silicon-based probes have limitations, including single-sided recording sites and intrinsic instabilities due to the probe stiffness. Here, we demonstrate high-performance neural recording using double-sided three-dimensional (3D) electrodes integrated in an ultraflexible bioinspired open mesh structure, allowing electrodes to sample fully the 3D interconnected tissue of the brain. In vivo electrophysiological recording using 3D electrodes shows statistically significant increases in the number of neurons per electrode, average spike amplitudes, and signal to noise ratios in comparison to standard two-dimensional electrodes, while achieving stable detection of single-neuron activity over months. The capability of these 3D electrodes is further shown for chronic recording from retinal ganglion cells in mice. This approach opens new opportunities for a comprehensive 3D interrogation, stimulation, and understanding of the complex circuitry of the brain and other electrogenic tissues in live animals over extended time periods.

A feedforward inhibitory premotor circuit for auditory-vocal interactions in zebra finches

Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.

Adaptive erasure of spurious sequences in sensory cortical circuits

Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas. However, it is unknown how cortical circuits avoid the converse problem: producing spurious sequences that are not reflecting sequences in their inputs. We develop methods to quantify and study sequentiality in neural responses. We show that recurrent circuit responses generally include spurious sequences, which are specifically prevented in circuits that obey two widely known features of cortical microcircuit organization: Dale’s law and Hebbian connectivity. In particular, spike-timing-dependent plasticity in excitation-inhibition networks leads to an adaptive erasure of spurious sequences. We tested our theory in multielectrode recordings from the visual cortex of awake ferrets. Although responses to natural stimuli were largely non-sequential, responses to artificial stimuli initially included spurious sequences, which diminished over extended exposure. These results reveal an unexpected role for Hebbian experience-dependent plasticity and Dale’s law in sensory cortical circuits.

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Recurrent circuits generate spurious sequences without sequential inputs

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A principled measure of total sequentiality in population responses is developed

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Theory predicts that Hebbian plasticity should abolish spurious sequences

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Spurious sequences in the visual cortex diminish with experience

A novel reticular node in the brainstem synchronizes neonatal mouse crying with breathing

Human speech can be divided into short, rhythmically timed elements, similar to syllables within words. Even our cries and laughs, as well as the vocalizations of other species, are periodic. However, the cellular and molecular mechanisms underlying the tempo of mammalian vocalizations remain unknown. Furthermore, even the core cells that produce vocalizations remain ill-defined. Here, we describe rhythmically timed neonatal mouse vocalizations that occur within single breaths and identify a brainstem node that is necessary for and sufficient to structure these cries, which we name the intermediate reticular oscillator (iRO). We show that the iRO acts autonomously and sends direct inputs to key muscles and the respiratory rhythm generator in order to coordinate neonatal vocalizations with breathing, as well as paces and patterns these cries. These results reveal that a novel mammalian brainstem oscillator embedded within the conserved breathing circuitry plays a central role in the production of neonatal vocalizations.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.

How do humans flexibly adapt action sequences? For instance, musicians can learn a song and quickly speed up or slow down the tempo, or even play the song following a completely different rhythm (e.g., a rock song using a bossa nova rhythm). In this work, we build a biologically plausible network of cortico-basal ganglia interactions that explains how this temporal flexibility may emerge in the brain. Crucially, our model factorizes sequence order and action timing, respectively represented in cortical and basal ganglia dynamics. This factorization allows full temporal flexibility, i.e. the timing of a learned action sequence can be recomposed without interfering with the order of the sequence. As such, our model is capable of learning asynchronous action sequences, and flexibly shift, rescale, and recompose them, while accounting for biological data.

Chronic recording of cortical activity underlying vocalization in awake minipigs

BACKGROUND

Investigating brain dynamics underlying vocal production in animals is a powerful way to inform on the neural bases of human speech. In particular, brain networks underlying vocal production in non-human primates show striking similarities with the human speech production network. However, despite increasing findings also in birds and more recently in rodents, the extent to which the primate vocal cortical network model generalizes to other non-primate mammals remains unclear. Especially, no domestic species has yet been proposed to investigate vocal brain activity using electrophysiological approaches.

NEW METHOD

In the present study, we introduce a novel experimental paradigm to identify the cortical dynamics underlying vocal production in behaving minipigs. A key problem to chronically implant cortical probes in pigs is the presence and growth of frontal sinuses extending caudally to the parietal bone and preventing safe access to neural structures with conventional craniotomy in adult animals.

RESULTS

Here we first show that implantations of soft ECoG grids can be done safely using conventional craniotomy in minipigs younger than 5 months, a period when sinuses are not yet well developed. Using wireless recordings in behaving animals, we further show activation of the motor and premotor cortex around the onset of vocal production of grunts, the most common vocalization of pigs.

CONCLUSION

These results suggest that minipigs, which are very loquacious and social animals, can be a good experimental large animal model to study the cortical bases of vocal production.

Sleep replay reveals premotor circuit structure for a skilled behavior

Neural circuits often exhibit sequences of activity, but the contribution of local networks to their generation remains unclear. In the zebra finch, song-related premotor sequences within HVC may result from some combination of local connectivity and long-range thalamic inputs from nucleus uvaeformis (Uva). Because lesions to either structure abolish song, we examine "sleep replay" using high-density recording methods to reconstruct precise song-related events. Replay activity persists after the upstream nucleus interfacialis of the nidopallium is lesioned and slows when HVC is cooled, demonstrating that HVC provides temporal structure for these events. To further gauge the importance of intra-HVC connectivity for shaping network dynamics, we lesion Uva during sleep and find that residual replay sequences could span syllable boundaries, supporting a model in which HVC can propagate sequences throughout the duration of the song. Our results highlight the power of studying offline activity to investigate behaviorally relevant circuit organization.

Local field potentials in a pre-motor region predict learned vocal sequences

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors.

Vocalizations, such as speech and song, are a motor process that requires the coordination of numerous muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing; it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity presumptively from HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Furthermore, given the similarities of the songbird and human motor-vocal systems, these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

A memory-driven auditory program ensures selective and precise vocal imitation in zebra finches

In the vocal learning model, the juvenile first memorizes a model sound, and the imprinted memory gradually converts into vocal-motor output during the sensorimotor integration. However, early acquired memory may not precisely represent the fine structures of a model sound. How do juveniles ensure precise model imitation? Here we show that juvenile songbirds develop an auditory learning program by actively and attentively engaging with tutor’s singing during the sensorimotor phase. The listening/approaching behavior requires previously acquired model memory and the individual variability of approaching behavior correlates with the precision of tutor song imitation. Moreover, it is modulated by dopamine and associated with forebrain regions for sensory processing. Overall, precise vocal learning may involve two steps of auditory processing: a passive imprinting of model memory occurs during the early sensory period; the previously acquired memory then guides an active and selective engagement of the re-exposed model to fine tune model imitation.

Wan-Chun Liu et al. demonstrate that the sensory phase of vocal learning in zebra finches is split across two stages: (1) passive listening and formation of a memory, and (2) active listening and behavioral engagement of juveniles with adult tutors. Furthermore, they show that approach behavior is correlated with song imitation quality, and immediate early gene expression in the caudal medial nidopallium linked to auditory behavior.

Neurogenomic insights into the behavioral and vocal development of the zebra finch

The zebra finch (Taeniopygia guttata) is a socially monogamous and colonial opportunistic breeder with pronounced sexual differences in singing and plumage coloration. Its natural history has led to it becoming a model species for research into sex differences in vocal communication, as well as behavioral, neural and genomic studies of imitative auditory learning. As scientists tap into the genetic and behavioral diversity of both wild and captive lineages, the zebra finch will continue to inform research into culture, learning, and social bonding, as well as adaptability to a changing climate.

Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences

Sequential activation of neurons has been observed during various behavioral and cognitive processes, but the underlying circuit mechanisms remain poorly understood. Here, we investigate premotor sequences in HVC (proper name) of the adult zebra finch forebrain that are central to the performance of the temporally precise courtship song. We use high-density silicon probes to measure song-related population activity, and we compare these observations with predictions from a range of network models. Our results support a circuit architecture in which heterogeneous delays between sequentially active neurons shape the spatiotemporal patterns of HVC premotor neuron activity. We gauge the impact of several delay sources, and we find the primary contributor to be slow conduction through axonal collaterals within HVC, which typically adds between 1 and 7.5 ms for each link within the sequence. Thus, local axonal "delay lines" can play an important role in determining the dynamical repertoire of neural circuits.

Differential development of myelin in zebra finch song nuclei

Songbirds learn vocalizations by hearing and practicing songs. As song develops, the tempo becomes faster and more precise. In the songbird brain, discrete nuclei form interconnected myelinated circuits that control song acquisition and production. The myelin sheath increases the speed of action potential propagation by insulating the axons of neurons and by reducing membrane capacitance. As the brain develops, myelin increases in density, but the time course of myelin development across discrete song nuclei has not been systematically studied in a quantitative fashion. We tested the hypothesis that myelination develops differentially across time and song nuclei. We examined myelin development in the brains of the zebra finch (Taeniopygia guttata) from chick at posthatch day (d) 8 to adult (up to 147 d) in five major song nuclei: HVC (proper name), robust nucleus of the arcopallium (RA), Area X, lateral magnocellular nucleus of the anterior nidopallium, and medial portion of the dorsolateral thalamic nucleus (DLM). All of these nuclei showed an increase in the density of myelination during development but at different rates and to different final degrees. Exponential curve fits revealed that DLM showed earlier myelination than other nuclei, and HVC showed the slowest myelination of song nuclei. Together, these data show differential maturation of myelination in different portions of the song system. Such differential maturation would be well placed to play a role in regulating the development of learned song.

Addition of new neurons and the emergence of a local neural circuit for precise timing

During development, neurons arrive at local brain areas in an extended period of time, but how they form local neural circuits is unknown. Here we computationally model the emergence of a network for precise timing in the premotor nucleus HVC in songbird. We show that new projection neurons, added to HVC post hatch at early stages of song development, are recruited to the end of a growing feedforward network. High spontaneous activity of the new neurons makes them the prime targets for recruitment in a self-organized process via synaptic plasticity. Once recruited, the new neurons fire readily at precise times, and they become mature. Neurons that are not recruited become silent and replaced by new immature neurons. Our model incorporates realistic HVC features such as interneurons, spatial distributions of neurons, and distributed axonal delays. The model predicts that the birth order of the projection neurons correlates with their burst timing during the song.

Functions of local neural circuits depend on their specific network structures, but how the networks are wired is unknown. We show that such structures can emerge during development through a self-organized process, during which the network is wired by neuron-by-neuron recruitment. This growth is facilitated by steady supply of immature neurons, which are highly excitable and plastic. We suggest that neuron maturation dynamics is an integral part of constructing local neural circuits.

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or "practice." Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time.NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain's code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird's song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song.

How is flexible electronics advancing neuroscience research?

Innovative neurotechnology must be leveraged to experimentally answer the multitude of pressing questions in modern neuroscience. Driven by the desire to address the existing neuroscience problems with newly engineered tools, we discuss in this review the benefits of flexible electronics for neuroscience studies. We first introduce the concept and define the properties of flexible and stretchable electronics. We then categorize the four dimensions where flexible electronics meets the demands of modern neuroscience: chronic stability, interfacing multiple structures, multi-modal compatibility, and neuron-type-specific recording. Specifically, with the bending stiffness now approaching that of neural tissue, implanted flexible electronic devices produce little shear motion, minimizing chronic immune responses and enabling recording and stimulation for months, and even years. The unique mechanical properties of flexible electronics also allow for intimate conformation to the brain, the spinal cord, peripheral nerves, and the retina. Moreover, flexible electronics enables optogenetic stimulation, microfluidic drug delivery, and neural activity imaging during electrical stimulation and recording. Finally, flexible electronics can enable neuron-type identification through analysis of high-fidelity recorded action potentials facilitated by its seamless integration with the neural circuitry. We argue that flexible electronics will play an increasingly important role in neuroscience studies and neurological therapies via the fabrication of neuromorphic devices on flexible substrates and the development of enhanced methods of neuronal interpenetration.

Actor-critic reinforcement learning in the songbird

It feels rewarding to ace your opponent on match point. Here, we propose common mechanisms underlie reward and performance learning. First, when a singing bird unexpectedly hits the right note, its dopamine (DA) neurons are activated as when a thirsty monkey receives an unexpected juice reward. Second, these DA signals reinforce vocal variations much as they reinforce stimulus-response associations. Third, limbic inputs to DA neurons signal the predicted quality of song syllables much like they signal the predicted reward value of a place or a stimulus during foraging. Finally, songbirds may solve difficult problems in reinforcement learning - such as credit assignment and catastrophic forgetting - with node perturbation and consolidation of reinforced vocal patterns in motor cortical circuits. Consolidation occurs downstream of a canonical 'actor-critic' circuit motif that learns to maximize performance quality in essentially the same way it learns to maximize reward: by computing and learning from prediction errors.

An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units

How are brain circuits constructed to achieve complex goals? The brains of young songbirds develop motor circuits that achieve the goal of imitating a specific tutor song to which they are exposed. Here, we set out to examine how song-generating circuits may be influenced early in song learning by a cortical region (NIf) at the interface between auditory and motor systems. Single-unit recordings reveal that, during juvenile babbling, NIf neurons burst at syllable onsets, with some neurons exhibiting selectivity for particular emerging syllable types. When juvenile birds listen to their tutor, NIf neurons are also activated at tutor syllable onsets, and are often selective for particular syllable types. We examine a simple computational model in which tutor exposure imprints the correct number of syllable patterns as ensembles in an interconnected NIf network. These ensembles are then reactivated during singing to train a set of syllable sequences in the motor network.

Young songbirds learn to imitate their parents’ songs. Here, the authors find that, in baby birds, neurons in a brain region at the interface of auditory and motor circuits signal the onsets of song syllables during both tutoring and babbling, suggesting a specific neural mechanism for vocal imitation.

Temporal structure of mouse courtship vocalizations facilitates syllable labeling

Mice emit sequences of ultrasonic vocalizations (USVs) but little is known about the rules governing their temporal order and no consensus exists on the classification of USVs into syllables. To address these questions, we recorded USVs during male-female courtship and found a significant temporal structure. We labeled USVs using three popular algorithms and found that there was no one-to-one relationships between their labels. As label assignment affects the high order temporal structure, we developed the Syntax Information Score (based on information theory) to rank labeling algorithms based on how well they predict the next syllable in a sequence. Finally, we derived a novel algorithm (Syntax Information Maximization) that utilizes sequence statistics to improve the clustering of individual USVs with respect to the underlying sequence structure. Improvement in USV classification is crucial for understanding neural control of vocalization. We demonstrate that USV syntax holds valuable information towards achieving this goal.

Hidden neural states underlie canary song syntax

Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identity and order of past actions. Canary songs are comprised of repeated syllables, called phrases, and the ordering of these phrases follows long-range rules¹, where the choice of what to sing depends on song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC^2–4 as canaries explore various phrase sequences in their repertoire. We find neurons that encode past transitions, extending over 4 phrases and spanning up to 4 seconds and 40 syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than a single song history, and occur mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that HVC dynamics includes “hidden states” not reflected in ongoing behavior – states that carry information about prior actions. These states provide a possible substrate to control syntax transitions governed by long-range rules.

Dynamical model for the neural activity of singing Serinus canaria

Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the "song system"), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus. This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.

High-fidelity continuum modeling predicts avian voiced sound production

Voiced sound production is the primary form of acoustic communication in terrestrial vertebrates, particularly birds and mammals, including humans. Developing a causal physics-based model that ultimately links descending vocal motor control to tissue vibration and sound requires embodied approaches that include realistic representations of voice physiology. Here, we first implement and then experimentally test a high-fidelity three-dimensional (3D) continuum model for voiced sound production in birds. Driven by individual-based physiologically quantifiable inputs, combined with noninvasive inverse methods for tissue material parameterization, our model accurately predicts observed key vibratory and acoustic performance traits. These results demonstrate that realistic models lead to accurate predictions and support the continuum model approach as a critical tool toward a causal model of voiced sound production.

Manipulations of inhibition in cortical circuitry differentially affect spectral and temporal features of Bengalese finch song

The interplay between inhibition and excitation can regulate behavioral expression and control, including the expression of communicative behaviors like birdsong. Computational models postulate varying degrees to which inhibition within vocal motor circuitry influences birdsong, but few studies have tested these models by manipulating inhibition. Here we enhanced and attenuated inhibition in the cortical nucleus HVC (used as proper name) of Bengalese finches (Lonchura striata var. domestica). Enhancement of inhibition (with muscimol) in HVC dose-dependently reduced the amount of song produced. Infusions of higher concentrations of muscimol caused some birds to produce spectrally degraded songs, whereas infusions of lower doses of muscimol led to the production of relatively normal (nondegraded) songs. However, the spectral and temporal structures of these nondegraded songs were significantly different from songs produced under control conditions. In particular, muscimol infusions decreased the frequency and amplitude of syllables, increased various measures of acoustic entropy, and increased the variability of syllable structure. Muscimol also increased sequence durations and the variability of syllable timing and syllable sequencing. Attenuation of inhibition (with bicuculline) in HVC led to changes to song distinct from and often opposite to enhancing inhibition. For example, in contrast to muscimol, bicuculline infusions increased syllable amplitude, frequency, and duration and decreased the variability of acoustic features. However, like muscimol, bicuculline increased the variability of syllable sequencing. These data highlight the importance of inhibition to the production of stereotyped vocalizations and demonstrate that changes to neural dynamics within cortical circuitry can differentially affect spectral and temporal features of song.NEW & NOTEWORTHY We reveal that manipulations of inhibition in the cortical nucleus HVC affect the structure, timing, and sequencing of syllables in Bengalese finch song. Enhancing and blocking inhibition led to opposite changes to the acoustic structure and timing of vocalizations, but both caused similar changes to vocal sequencing. These data provide support for computational models of song control but also motivate refinement of existing models to account for differential effects on syllable structure, timing, and sequencing.

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.

Unsupervised Learning of Persistent and Sequential Activity

Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be “learned” by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism is necessary. We show that a generalized version of the standard multiplicative homeostatic plasticity (Renart et al., 2003; Toyoizumi et al., 2014) stabilizes learning by effectively masking excitatory connections during stimulation and unmasking those connections during retrieval. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.

Sound sequences in birdsong: how much do birds really care?

The complex and melodic nature of many birds' songs has raised interest in potential parallels between avian vocal sequences and human speech. The similarities between birdsong and speech in production and learning are well established, but surprisingly little is known about how birds perceive song sequences. One popular laboratory songbird, the zebra finch (Taeniopygia guttata), has recently attracted attention as an avian model for human speech, in part because the male learns to produce the individual elements in its song motif in a fixed sequence. But psychoacoustic evidence shows that adult zebra finches are relatively insensitive to the sequential features of song syllables. Instead, zebra finches and other birds seem to be exquisitely sensitive to the acoustic details of individual syllables to a degree that is beyond human hearing capacity. Based on these findings, we present a finite-state model of zebra finch perception of song syllable sequences and discuss the rich informational capacity of their vocal system. Furthermore, we highlight the abilities of budgerigars (Melopsittacus undulatus), a parrot species, to hear sequential features better than zebra finches and suggest that neurophysiological investigations comparing these species could prove fruitful for uncovering neural mechanisms for auditory sequence perception in human speech. This article is part of the theme issue 'What can animal communication teach us about human language?'

Inception of memories that guide vocal learning in the songbird

Animals learn many complex behaviors by emulating the behavior of more experienced individuals. This essential, yet still poorly understood, form of learning relies on the ability to encode lasting memories of observed behaviors. We identified a vocal-motor pathway in the zebra finch where memories that guide learning of song-element durations can be implanted. Activation of synapses in this pathway seeds memories that guide learning of song-element duration and can override learning from social interactions with other individuals. Genetic lesions of this circuit after memory formation, however, do not disrupt subsequent song imitation, which suggests that these memories are stored at downstream synapses. Thus, activity at these sensorimotor synapses can bypass learning from auditory and social experience and embed memories that guide learning of song timing.

Contributions of the basal ganglia to action sequence learning and performance

Animals engage in intricately woven and choreographed action sequences that are constructed from trial-and-error learning. The mechanisms by which the brain links together individual actions which are later recalled as fluid chains of behavior are not fully understood, but there is broad consensus that the basal ganglia play a crucial role in this process. This paper presents a comprehensive review of the role of the basal ganglia in action sequencing, with a focus on whether the computational framework of reinforcement learning can capture key behavioral features of sequencing and the neural mechanisms that underlie them. While a simple neurocomputational model of reinforcement learning can capture key features of action sequence learning, this model is not sufficient to capture goal-directed control of sequences or their hierarchical representation. The hierarchical structure of action sequences, in particular, poses a challenge for building better models of action sequencing, and it is in this regard that further investigations into basal ganglia information processing may be informative.

Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex

Recent studies reveal the occasional impact of single neurons on surround firing statistics and even simple behaviors. Exploiting the advantages of a simple cortex, we examined the influence of single pyramidal neurons on surrounding cortical circuits. Brief activation of single neurons triggered reliable sequences of firing in tens of other excitatory and inhibitory cortical neurons, reflecting cascading activity through local networks, as indicated by delayed yet precisely timed polysynaptic subthreshold potentials. The evoked patterns were specific to the pyramidal cell of origin, extended over hundreds of micrometers from their source, and unfolded over up to 200 ms. Simultaneous activation of pyramidal cell pairs indicated balanced control of population activity, preventing paroxysmal amplification. Single cortical pyramidal neurons can thus trigger reliable postsynaptic activity that can propagate in a reliable fashion through cortex, generating rapidly evolving and non-random firing sequences reminiscent of those observed in mammalian hippocampus during "replay" and in avian song circuits.

Nanoenabled Direct Contact Interfacing of Syringe-Injectable Mesh Electronics

Polymer-based electronics with low bending stiffnesses and high flexibility, including recently reported macroporous syringe-injectable mesh electronics, have shown substantial promise for chronic studies of neural circuitry in the brains of live animals. A central challenge for exploiting these highly flexible materials for in vivo studies has centered on the development of efficient input/output (I/O) connections to an external interface with high yield, low bonding resistance, and long-term stability. Here we report a new paradigm applied to the challenging case of injectable mesh electronics that exploits the high flexibility of nanoscale thickness two-sided metal I/O pads that can deform and contact standard interface cables in high yield with long-term electrical stability. First, we describe the design and facile fabrication of two-sided metal I/O pads that allow for contact without regard to probe orientation. Second, systematic studies of the contact resistance as a function of I/O pad design and mechanical properties demonstrate the key role of the I/O pad bending stiffness in achieving low-resistance stable contacts. Additionally, computational studies provide design rules for achieving high-yield multiplexed contact interfacing in the case of angular misalignment such that adjacent channels are not shorted. Third, the in vitro measurement of 32-channel mesh electronics probes bonded to interface cables using the direct contact method shows a reproducibly high yield of electrical connectivity. Finally, in vivo experiments with 32-channel mesh electronics probes implanted in live mice demonstrate the chronic stability of the direct contact interface, enabling consistent tracking of single-unit neural activity over at least 2 months without a loss of channel recording. The direct contact interfacing methodology paves the way for scalable long-term connections of multiplexed mesh electronics neural probes for neural recording and modulation and moreover could be used to facilitate a scalable interconnection of other flexible electronics in biological studies and therapeutic applications.

Developmental modulation and predictability of age-dependent vocal plasticity in adult zebra finches

Predicting the nature of behavioral plasticity can provide insight into mechanisms of behavioral expression and control. Songbirds like the zebra finch rely on vocal signals for communication, and the performance of these signals demonstrate considerable plasticity over development. Traditionally, these signals were thought to be fixed in adulthood, but recent studies have revealed significant age-dependent changes to spectral and temporal features of song in adult songbirds. A number of age-dependent changes to song resemble acute changes to adult song performance across social contexts (e.g., when an adult male sings to a female relative to when he sings in isolation). The ability of variation in social context-dependent changes to predict variation in age-dependent plasticity would suggest shared mechanisms, but little is known about this predictability. In addition, although developmental experiences can shape adult plasticity, little is known about the extent to which social interactions during development affect age-dependent change to adult song. To this end, we systematically analyzed age- and context-dependent changes to adult zebra finch song, and then examined the degree to which age-dependent changes varied across birds that were social or non-socially tutored birds and to which social context-dependent changes predicted age-dependent changes. Non-socially tutored birds showed more dramatic changes to the broad structure of their motif over time than socially tutored birds, but non-socially and socially tutored birds did not differ in the extent of changes to various spectral and temporal features of song. Overall, we found that adult zebra finches produced longer and more spectrally stereotyped songs when they were older than when they were younger. Moreover, regardless of developmental tutoring, individual variation in age-dependent changes to song bout duration and syllable repetition were predicted by variation in social context-dependent changes to these features. These data indicate that social experiences during development can shape some aspects of adult plasticity and that acute context-dependent and long-term age-dependent changes to some song features could be mediated by modifications within similar neural substrates.