Daniel Margoliash

Temporal Hierarchical Control of Singing in Birds

Songs of birds comprise hierarchical sets of vocal gestures. In zebra finches, songs include notes and syllables (groups of notes) delivered in fixed sequences. During singing, premotor neurons in the forebrain nucleus HVc exhibited reliable changes in activity rates whose patterns were uniquely associated with syllable identity. Neurons in the forebrain nucleus robustus archistriatalis, which receives input from the HVc, exhibited precisely timed and structured bursts of activity that were uniquely associated with note identity. Hence, units of vocal behavior are represented hierarchically in the avian forebrain. The representation of temporal sequences at each level of the hierarchy may be established by means of a decoding process involving interactions of higher level input with intrinsic local circuitry. Behavior is apparently represented by precise temporal patterning of spike trains at lower levels of the hierarchy.

Recursive syntactic pattern learning by songbirds

Humans regularly produce new utterances that are understood by other members of the same language community. Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the acceptable structure of utterances. The recursive, hierarchical embedding of language units (for example, words or phrases within shorter sentences) that is part of the ability to construct new utterances minimally requires a ‘context-free’ grammar that is more complex than the ‘finite-state’ grammars thought sufficient to specify the structure of all non-human communication signals. Recent hypotheses make the central claim that the capacity for syntactic recursion forms the computational core of a uniquely human language faculty. Here we show that European starlings (Sturnus vulgaris) accurately recognize acoustic patterns defined by a recursive, self-embedding, context-free grammar. They are also able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns. Thus, the capacity to classify sequences from recursive, centre-embedded grammars is not uniquely human. This finding opens a new range of complex syntactic processing mechanisms to physiological investigation.

Consolidation during sleep of perceptual learning of spoken language

Memory consolidation resulting from sleep has been seen broadly: in verbal list learning, spatial learning, and skill acquisition in visual and motor tasks. These tasks do not generalize across spatial locations or motor sequences, or to different stimuli in the same location. Although episodic rote learning constitutes a large part of any organisms learning, generalization is a hallmark of adaptive behaviour. In speech, the same phoneme often has different acoustic patterns depending on context. Training on a small set of words improves performance on novel words using the same phonemes but with different acoustic patterns, demonstrating perceptual generalization. Here we show a role of sleep in the consolidation of a naturalistic spoken-language learning task that produces generalization of phonological categories across different acoustic patterns. Recognition performance immediately after training showed a significant improvement that subsequently degraded over the span of a days retention interval, but completely recovered following sleep. Thus, sleep facilitates the recovery and subsequent retention of material learned opportunistically at any time throughout the day. Performance recovery indicates that representations and mappings associated with generalization are refined and stabilized during sleep.

Neuronal populations and single cells representing learned auditory objects.

The neural representations associated with learned auditory behaviours, such as recognizing individuals based on their vocalizations, are not well described. Higher vertebrates learn to recognize complex conspecific vocalizations that comprise sequences of easily identified, naturally occurring auditory objects, which should facilitate the analysis of higher auditory pathways. Here we describe the first example of neurons selective for learned conspecific vocalizations in adult animals—in starlings that have been trained operantly to recognize conspecific songs. The neuronal population is found in a non-primary forebrain auditory region, exhibits increased responses to the set of learned songs compared with novel songs, and shows differential responses to categories of learned songs based on recognition training contingencies. Within the population, many cells respond highly selectively to a subset of specific motifs (acoustic objects) present only in the learned songs. Such neuronal selectivity may contribute to song-recognition behaviour, which in starlings is sensitive to motif identity. In this system, both top-down and bottom-up processes may modify the tuning properties of neurons during recognition learning, giving rise to plastic representations of behaviourally meaningful auditory objects.

Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study

The performance of two techniques is compared for automated recognition of bird song units from continuous recordings. The advantages and limitations of dynamic time warping (DTW) and hidden Markov models (HMMs) are evaluated on a large database of male songs of zebra finches (Taeniopygia guttata) and indigo buntings (Passerina cyanea), which have different types of vocalizations and have been recorded under different laboratory conditions. Depending on the quality of recordings and complexity of song, the DTW-based technique gives excellent to satisfactory performance. Under challenging conditions such as noisy recordings or presence of confusing short-duration calls, good performance of the DTW-based technique requires careful selection of templates that may demand expert knowledge. Because HMMs are trained, equivalent or even better performance of HMMs can be achieved based only on segmentation and labeling of constituent vocalizations, albeit with many more training examples than DTW templates. One weakness in HMM performance is the misclassification of short-duration vocalizations or song units with more variable structure (e.g., some calls, and syllables of plastic songs). To address these and other limitations, new approaches for analyzing bird vocalizations are discussed.

Functional Organization of Forebrain Pathways for Song Production and Perception

This article reviews the organization of the forebrain nuclei of the avian song system. Particular emphasis is placed on recent physiologic recordings from awake behaving adult birds while they sing, call, and listen to broadcasts of acoustic stimuli. The neurons in the descending motor pathway (HVc and RA) are organized in a hierarchical arrangement of temporal units of song production, with HVc neurons representing syllables and RA neurons representing notes. The nuclei Uva and NIf, which are afferent to HVc, may help organize syllables into larger units of vocalization. HVc and RA are also active during production of all calls. The patterns of activity associated with calls differ between learned calls and those that are innately specified, and give insight into the interactions between the forebrain and midbrain during calling, as well as into the evolutionary origins of the song system. Neurons in Area X, the first part of the anterior forebrain pathway leading from HVc to RA, are also active during singing. Many HVc neurons are also auditory, exhibiting selectivity for learned acoustic parameters of the individual birds own song (BOS). Similar auditory responses are also observed in RA and Area X in anesthetized birds. In contrast to HVc, however, auditory responses in RA are very weak or absent in awake birds under our experimental paradigm, but are uncovered when birds are anesthetized. Thus, the roles of both pathways beyond HVc in adult birds is under review. In particular, theories hypothesizing a role for the descending motor pathway (RA and below) in adult song perception do not appear to obtain. The data also suggest that the anterior forebrain pathway has a greater motor role than previously considered. We suggest that a major role of the anterior forebrain pathway is to resolve the timing mismatch between motor program readout and sensory feedback, thereby facilitating motor programming during birdsong learning. Pathways afferent to HVc may participate more in sensory acquisition and sensorimotor learning during song development than is commonly assumed.

Template‐based automatic recognition of birdsong syllables from continuous recordings

The application of dynamic time warping (DTW) to the automated analysis of continuous recordings of animal vocalizations is evaluated. The DTW algorithm compares an input signal with a set of predefined templates representative of categories chosen by the investigator. It directly compares signal spectrograms, and identifies constituents and constituent boundaries, thus permitting the identification of a broad range of signals and signal components. When applied to vocalizations of an indigo bunting (Passerina cyanea) and a zebra finch (Taeniopygia guttata) collected from a low-clutter, low-noise environment, the recognizer identifies syllables in stereotyped songs and calls with greater than 97% accuracy. Syllables of the more variable and lower amplitude indigo bunting plastic song are identified with approximately 84% accuracy. Under restricted recordings conditions, this technique apparently has general applicability to analysis of a variety of animal vocalizations and can dramatically decrease the amount of time spent on manual identification of vocalizations.

Sleep and sensorimotor integration during early vocal learning in a songbird

Behavioural studies widely implicate sleep in memory consolidation in the learning of a broad range of behaviours. During sleep, brain regions are reactivated, and specific patterns of neural activity are replayed, consistent with patterns observed in previous waking behaviour. Birdsong learning is a paradigmatic model system for skill learning. Song development in juvenile zebra finches (Taeniopygia guttata) is characterized by sleep-dependent circadian fluctuations in singing behaviour, with immediate post-sleep deterioration in song structure followed by recovery later in the day. In sleeping adult birds, spontaneous bursting activity of forebrain premotor neurons in the robust nucleus of the arcopallium (RA) carries information about daytime singing. Here we show that, in juvenile zebra finches, playback during the day of an adult ‘tutor’ song induced profound and tutor-song-specific changes in bursting activity of RA neurons during the following night of sleep. The night-time neuronal changes preceded tutor-song-induced changes in singing, first observed the following day. Interruption of auditory feedback greatly reduced sleep bursting and prevented the tutor-song-specific neuronal remodelling. Thus, night-time neuronal activity is shaped by the interaction of the song model (sensory template) and auditory feedback, with changes in night-time activity preceding the onset of practice associated with vocal learning. We hypothesize that night-time bursting induces adaptive changes in premotor networks during sleep as part of vocal learning. By this hypothesis, adaptive changes driven by replay of sensory information at night and by evaluation of sensory feedback during the day interact to produce the complex circadian patterns seen early in vocal development.

Elemental gesture dynamics are encoded by song premotor cortical neurons

Quantitative biomechanical models can identify control parameters that are used during movements, and movement parameters that are encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics and upper-vocal-tract filtering to the songs of zebra finches. This reduces the dimensionality of singing dynamics, described as trajectories (motor ‘gestures’) in a space of syringeal pressure and tension. Here we assess model performance by characterizing the auditory response ‘replay’ of song premotor HVC neurons to the presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed within a few milliseconds of the extreme time points of the gesture trajectories. Thus, the HVC precisely encodes vocal motor output through activity at the times of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons is used as a ‘forward’ model, representing the sequence of gestures in song to make predictions on expected behaviour and evaluate feedback.

Consolidating the Effects of Waking and Sleep on Motor-Sequence Learning

Sleep is widely believed to play a critical role in memory consolidation. Sleep-dependent consolidation has been studied extensively in humans using an explicit motor-sequence learning paradigm. In this task, performance has been reported to remain stable across wakefulness and improve significantly after sleep, making motor-sequence learning the definitive example of sleep-dependent enhancement. Recent work, however, has shown that enhancement disappears when the task is modified to reduce task-related inhibition that develops over a training session, thus questioning whether sleep actively consolidates motor learning. Here we use the same motor-sequence task to demonstrate sleep-dependent consolidation for motor-sequence learning and explain the discrepancies in results across studies. We show that when training begins in the morning, motor-sequence performance deteriorates across wakefulness and recovers after sleep, whereas performance remains stable across both sleep and subsequent waking with evening training. This pattern of results challenges an influential model of memory consolidation defined by a time-dependent stabilization phase and a sleep-dependent enhancement phase. Moreover, the present results support a new account of the behavioral effects of waking and sleep on explicit motor-sequence learning that is consistent across a wide range of tasks. These observations indicate that current theories of memory consolidation that have been formulated to explain sleep-dependent performance enhancements are insufficient to explain the range of behavioral changes associated with sleep.