Brain stem feedback in a computational model of birdsong sequencing

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Network dynamics underlie learning and performance of birdsong

Understanding the sensorimotor control of the endless variety of human speech patterns stands as one of the apex problems in neuroscience. The capacity to learn - through imitation - to rapidly sequence vocal sounds in meaningful patterns is clearly one of the most derived of human behavioral traits. Selection pressure produced an analogous capacity in numerous species of vocal-learning birds, and due to an increasing appreciation for the cognitive and computational flexibility of avian cortex and basal ganglia, a general understanding of the forebrain network that supports the learning and production of birdsong is beginning to emerge. Here, we review recent advances in experimental studies of the zebra finch (Taeniopygia guttata), which offer new insights into the network dynamics that support this surprising analogue of human speech learning and production.

Interhemispheric dominance switching in a neural network model for birdsong

Male zebra finches produce a sequence-invariant set of syllables, separated by short inspiratory gaps. These songs are learned from an adult tutor and maintained throughout life, making them a tractable model system for learned, sequentially ordered behaviors, particularly speech production. Moreover, much is known about the cortical, thalamic, and brain stem areas involved in producing this behavior, with the premotor cortical nucleus HVC (proper name) being of primary importance. In a previous study, our group developed a behavioral neural network model for birdsong constrained by the structural connectivity of the song system, the signaling properties of individual neurons and circuits, and circuit-breaking behavioral studies. Here we describe a more computationally tractable model and use it to explain the behavioral effects of unilateral cooling and electrical stimulations of HVC on song production. The model demonstrates that interhemispheric switching of song control is sufficient to explain these results, consistent with the hypotheses proposed when the experiments were initially conducted. Finally, we use the model to make testable predictions that can be used to validate the model framework and explain the effects of other perturbations of the song system, such as unilateral ablations of the primary input and output nuclei of HVC.

NEW & NOTEWORTHY In this report, we propose a two-hemisphere neural network model for the bilaterally symmetrical song system underlying birdsong in the male zebra finch. This model captures the behavioral effects of unilateral cooling and electrical stimulations of the premotor cortical nucleus HVC during song production, supporting the hypothesis of interhemispheric switching of song control. We use the model to make testable predictions regarding the behavioral effects of other unilateral perturbations to the song system.

Nonlinear dynamics in the study of birdsong

Birdsong, a rich and complex behavior, is a stellar model to understand a variety of biological problems, from motor control to learning. It also enables us to study how behavior emerges when a nervous system, a biomechanical device and the environment interact. In this review, I will show that many questions in the field can benefit from the approach of nonlinear dynamics, and how birdsong can inspire new directions for research in dynamics.

Rhythmic syllable-related activity in a songbird motor thalamic nucleus necessary for learned vocalizations

Birdsong is a complex behavior that exhibits hierarchical organization. While the representation of singing behavior and its hierarchical organization has been studied in some detail in avian cortical premotor circuits, our understanding of the role of the thalamus in adult birdsong is incomplete. Using a combination of behavioral and electrophysiological studies, we seek to expand on earlier work showing that the thalamic nucleus Uvaeformis (Uva) is necessary for the production of stereotyped, adult song in zebra finch (Taeniopygia guttata). We confirm that complete bilateral lesions of Uva abolish singing in the ‘directed’ social context, but find that in the ‘undirected’ social context, such lesions result in highly variable vocalizations similar to early babbling song in juvenile birds. Recordings of neural activity in Uva reveal strong syllable-related modulation, maximally active prior to syllable onsets and minimally active prior to syllable offsets. Furthermore, both song and Uva activity exhibit a pronounced coherent modulation at 10Hz—a pattern observed in downstream premotor areas in adult and, even more prominently, in juvenile birds. These findings are broadly consistent with the idea that Uva is critical in the sequential activation of behavioral modules in HVC.

A distributed neural network model for the distinct roles of medial and lateral HVC in zebra finch song production

Male zebra finches produce a song consisting of a canonical sequence of syllables, learned from a tutor and repeated throughout its adult life. Much of the neural circuitry responsible for this behavior is located in the cortical premotor region HVC (acronym is name). In a recent study from our laboratory, we found that partial bilateral ablation of the medial portion of HVC has effects on the song that are qualitatively different from those of bilateral ablation of the lateral portion. In this report we describe a neural network organization that can explain these data, and in so doing suggests key roles for other brain nuclei in the production of song. We also suggest that syllables and the gaps between them are each coded separately by neural chains within HVC, and that the timing mechanisms for syllables and gaps are distinct. The design principles underlying this model assign distinct roles for medial and lateral HVC circuitry that explain the data on medial and lateral ablations. In addition, despite the fact that the neural coding of song sequence is distributed among several brain nuclei in our model, it accounts for data showing that cooling of HVC stretches syllables uniformly and to a greater extent than gaps. Finally, the model made unanticipated predictions about details of the effects of medial and lateral HVC ablations that were then confirmed by reanalysis of these previously acquired behavioral data.NEW & NOTEWORTHY Zebra finch song consists of a string of syllables repeated in a nearly invariant sequence. We propose a neural network organization that can explain recent data indicating that the medial and lateral portions of the premotor cortical nucleus HVC have different roles in zebra finch song production. Our model explains these data, as well as data on the effects on song of cooling HVC, and makes predictions that we test in the singing bird.

Temperature Manipulation in Songbird Brain Implicates the Premotor Nucleus HVC in Birdsong Syntax

Variable motor sequences of animals are often structured and can be described by probabilistic transition rules between action elements. Examples include the songs of many songbird species such as the Bengalese finch, which consist of stereotypical syllables sequenced according to probabilistic rules (song syntax). The neural mechanisms behind such rules are poorly understood. Here, we investigate where the song syntax is encoded in the brain of the Bengalese finch by rapidly and reversibly manipulating the temperature in the song production pathway. Cooling the premotor nucleus HVC (proper name) slows down the song tempo, consistent with the idea that HVC controls moment-to-moment timings of acoustic features in the syllables. More importantly, cooling HVC alters the transition probabilities between syllables. Cooling HVC reduces the number of repetitions of long-repeated syllables and increases the randomness of syllable sequences. In contrast, cooling the downstream motor area RA (robust nucleus of the acropallium), which is critical for singing, does not affect the song syntax. Unilateral cooling of HVC shows that control of syllables is mostly lateralized to the left HVC, whereas transition probabilities between the syllables can be affected by cooling HVC in either hemisphere to varying degrees. These results show that HVC is a key site for encoding song syntax in the Bengalese finch. HVC is thus involved both in encoding timings within syllables and in sequencing probabilistic transitions between syllables. Our finding suggests that probabilistic selections and fine-grained timings of action elements can be integrated within the same neural circuits.SIGNIFICANCE STATEMENT Many animal behaviors such as birdsong consist of variable sequences of discrete actions. Where and how the probabilistic rules of such sequences are encoded in the brain is poorly understood. We locally and reversibly cooled brain areas in songbirds during singing. Mild cooling of area HVC in the Bengalese finch brain-a premotor area homologous to the mammalian premotor cortex-alters the statistics of the syllable sequences, suggesting that HVC is critical for birdsong sequences. HVC is also known for controlling moment-to-moment timings within syllables. Our results show that timing and probabilistic sequencing of actions can share the same neural circuits in local brain areas.

Model of the songbird nucleus HVC as a network of central pattern generators

We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a "functional syllable unit" (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: 1) simultaneous firing wherein all inhibitory neurons fire simultaneously, and 2) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state 1 the activity of projection neurons is suppressed, while during state 2 patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other. Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.

A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

How do forebrain and brainstem circuits interact to produce temporally precise and reproducible behaviors? Birdsong is an elaborate, temporally precise, and stereotyped vocal behavior controlled by a network of forebrain and brainstem nuclei. An influential idea is that song premotor neurons in a forebrain nucleus (HVC) form a synaptic chain that dictates song timing in a top-down manner. Here we combine physiological, dynamical, and computational methods to show that song timing is not generated solely by a mechanism localized to HVC but instead is the product of a distributed and recurrent synaptic network spanning the forebrain and brainstem, of which HVC is a component.

Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition

Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences.

Repetitions are common in animal vocalizations. Songs of many songbirds contain syllables that repeat a variable number of times, with non-Markovian distributions of repeat counts. The neural mechanism underlying such syllable repetitions is unknown. In this work, we show that auditory feedback plays an important role in sustaining syllable repetitions in the Bengalese finch. Deafening reduces syllable repetitions and skews the repeat number distribution towards short repeats. These effects are explained with our computational model, which suggests that syllable repeats are initially sustained by auditory feedback to the neural networks that drive the syllable production. The feedback strength weakens as the syllable repeats, increasing the likelihood that the syllable repetition stops. Neural recordings confirm such adaptation of auditory feedback to the auditory-motor circuit in the Bengalese finch. Our results suggests that sensory feedback can directly impact repetitions in motor sequences, and may provide insights into neural mechanisms of speech disorders such as stuttering.

A circular model for song motor control in Serinus canaria

Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture.

Independent premotor encoding of the sequence and structure of birdsong in avian cortex

How the brain coordinates rapid sequences of learned behavior, such as human speech, remains a fundamental problem in neuroscience. Birdsong is a model of such behavior, which is learned and controlled by a neural circuit that spans avian cortex, basal ganglia, and thalamus. The songs of adult male zebra finches (Taeniopygia guttata), produced as rapid sequences of vocal gestures (syllables), are encoded by the cortical premotor region HVC (proper name). While the motor encoding of song within HVC has traditionally been viewed as unitary and distributed, we used an ablation technique to ask whether the sequence and structure of song are processed independently within HVC. Results revealed a functional topography across the medial-lateral axis of HVC. Bilateral ablation of medial HVC induced a positive disruption of song (increase in atypical syllable sequences), whereas bilateral ablation of lateral HVC induced a negative disruption (omission of individual syllables). Bilateral ablation of central HVC either had no effect on song or induced syllable omission, similar to lateral HVC ablation. We then investigated HVC connectivity and found parallel afferent and efferent pathways that transit medial and lateral HVC and converge at vocal motor cortex. In light of recent evidence that syntactic and lexical components of human speech are processed independently by neighboring regions of cortex (Menenti et al., 2012), our demonstration of anatomically distinct pathways that differentially process the sequence and structure of birdsong in parallel suggests that the vertebrate brain relies on a common approach to encode rapid sequences of vocal gestures.

Two neural streams, one voice: pathways for theme and variation in the songbird brain

Birdsong offers a unique model system to understand how a developing brain - once given a set of purely acoustic targets - teaches itself the vocal-tract gestures necessary to imitate those sounds. Like human infants, to juvenile male zebra finches (Taeniopygia guttata) falls the burden of initiating the vocal-motor learning of adult sounds. In both species, adult caregivers provide only a set of sounds to be imitated, with little or no information about the vocal-tract gestures used to produce the sounds. Here, we focus on the central control of birdsong and review the recent discovery that zebra finch song is under dual premotor control. Distinct forebrain pathways for structured (theme) and unstructured (variation) singing not only raise new questions about mechanisms of sensory-motor integration, but also provide a fascinating new research opportunity. A cortical locus for a motor memory of the learned song is now firmly established, meaning that anatomical, physiological, and computational approaches are poised to reveal the neural mechanisms used by the brain to compose the songs of birds.

The respiratory-vocal system of songbirds: anatomy, physiology, and neural control

This wide-ranging review presents an overview of the respiratory-vocal system in songbirds, which are the only other vertebrate group known to display a degree of respiratory control during song rivalling that of humans during speech; this despite the fact that the peripheral components of both the respiratory and vocal systems differ substantially in the two groups. We first provide a brief description of these peripheral components in songbirds (lungs, air sacs and respiratory muscles, vocal organ (syrinx), upper vocal tract) and then proceed to a review of the organization of central respiratory-related neurons in the spinal cord and brainstem, the latter having an organization fundamentally similar to that of the ventral respiratory group of mammals. The second half of the review describes the nature of the motor commands generated in a specialized "cortical" song control circuit and how these might engage brainstem respiratory networks to shape the temporal structure of song. We also discuss a bilaterally projecting "respiratory-thalamic" pathway that links the respiratory system to "cortical" song control nuclei. This necessary pathway for song originates in the brainstem's primary inspiratory center and is hypothesized to play a vital role in synchronizing song motor commands both within and across hemispheres.

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

The nucleus HVC (proper name) within the avian analog of mammal premotor cortex produces stereotyped instructions through the motor pathway leading to precise, learned vocalization by songbirds. Electrophysiological characterization of component HVC neurons is an important requirement in building a model to understand HVC function. The HVC contains three neural populations: neurons that project to the RA (robust nucleus of arcopallium), neurons that project to Area X (of the avian basal ganglia), and interneurons. These three populations are interconnected with specific patterns of excitatory and inhibitory connectivity, and they fire with characteristic patterns both in vivo and in vitro. We performed whole cell current-clamp recordings on HVC neurons within brain slices to examine their intrinsic firing properties and determine which ionic currents are responsible for their characteristic firing patterns. We also developed conductance-based models for the different neurons and calibrated the models using data from our brain slice work. These models were then used to generate predictions about the makeup of the ionic currents that are responsible for the different responses to stimuli. These predictions were then tested and verified in the slice using pharmacological manipulations. The model and the slice work highlight roles of a hyperpolarization-activated inward current (Ih), a low-threshold T-type Ca(2+) current (ICa-T), an A-type K(+) current (IA), a Ca(2+)-activated K(+) current (ISK), and a Na(+)-dependent K(+) current (IKNa) in driving the characteristic neural patterns observed in the three HVC neuronal populations. The result is an improved characterization of the HVC neurons responsible for song production in the songbird.

A reafferent and feed-forward model of song syntax generation in the Bengalese finch

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. We present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area. Propagating synfire activity in the HVC codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. The model also reproduces for the first time experimental findings on the influence of altered auditory feedback on the song syntax generation, and predicts song- and species-specific low frequency components in the LFP. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons.

A bird's eye view of neural circuit formation

Neural circuits underlying complex learned behaviors, such as speech in humans, develop under genetic constraints and in response to environmental influences. Little is known about the rules and mechanisms through which such circuits form. We argue that songbirds, with their discrete and well studied neural pathways underlying a complex and naturally learned behavior, provide a powerful model for addressing these questions. We briefly review current knowledge of how the song circuit develops during learning and discuss new possibilities for advancing the field given recent technological advances.

Inhibition and recurrent excitation in a computational model of sparse bursting in song nucleus HVC

The telencephalic premotor nucleus HVC is situated at a critical point in the pattern-generating premotor circuitry of oscine songbirds. A striking feature of HVC's premotor activity is that its projection neurons burst extremely sparsely. Here we present a computational model of HVC embodying several central hypotheses: 1) sparse bursting is generated in bistable groups of recurrently connected robust nucleus of the arcopallium (RA)-projecting (HVCRA) neurons; 2) inhibitory interneurons terminate bursts in the HVCRA groups; and 3) sparse sequences of bursts are generated by the propagation of waves of bursting activity along networks of HVCRA neurons. Our model of sparse bursting places HVC in the context of central pattern generators and cortical networks using inhibition, recurrent excitation, and bistability. Importantly, the unintuitive result that inhibitory interneurons can precisely terminate the bursts of HVCRA groups while showing relatively sustained activity throughout the song is made possible by a specific constraint on their connectivity. We use the model to make novel predictions that can be tested experimentally.