Mesoscopic patterns of neural activity support songbird cortical sequences

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.

Simultaneous dual-color calcium imaging in freely-behaving mice

Miniaturized fluorescence microscopes (miniscopes) enable imaging of calcium events from a large population of neurons in freely behaving animals. Traditionally, miniscopes have only been able to record from a single fluorescence wavelength. Here, we present a new open-source dual-channel Miniscope that simultaneously records two wavelengths in freely behaving animals. To enable simultaneous acquisition of two fluorescent wavelengths, we incorporated two CMOS sensors into a single Miniscope. To validate our dual-channel Miniscope, we imaged hippocampal CA1 region that co-expressed a dynamic calcium indicator (GCaMP) and a static nuclear signal (tdTomato) while mice ran on a linear track. Our results suggest that, even when neurons were registered across days using tdTomato signals, hippocampal spatial coding changes over time. In conclusion, our novel dual-channel Miniscope enables imaging of two fluorescence wavelengths with minimal crosstalk between the two channels, opening the doors to a multitude of new experimental possibilities.

Teaser

Novel open-source dual-channel Miniscope that simultaneously records two wavelengths with minimal crosstalk in freely behaving animals.

Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation

Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic input to neighboring, unmanipulated neurons in the same brain region. A model inspired by the behavioral and electrophysiological findings suggests that unsupervised single-cell and population-level homeostatic plasticity rules can support the functional restoration after large-scale disruption of networks that implement sequential dynamics. These observations suggest the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.

Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons

Maintaining motor skills is crucial for an animal's survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.

A custom-made AAV1 variant (AAV1-T593K) enables efficient transduction of Japanese quail neurons in vitro and in vivo

The widespread use of rodents in neuroscience has prompted the development of optimized viral variants for transduction of brain cells, in vivo. However, many of the viruses developed are less efficient in other model organisms, with birds being among the most resistant to transduction by current viral tools. Resultantly, the use of genetically-encoded tools and methods in avian species is markedly lower than in rodents; likely holding the field back. We sought to bridge this gap by developing custom viruses towards the transduction of brain cells of the Japanese quail. We first develop a protocol for culturing primary neurons and glia from quail embryos, followed by characterization of cultures via immunostaining, single cell mRNA sequencing, patch clamp electrophysiology and calcium imaging. We then leveraged the cultures for the rapid screening of various viruses, only to find that all yielded poor to no infection of cells in vitro. However, few infected neurons were obtained by AAV1 and AAV2. Scrutiny of the sequence of the AAV receptor found in quails led us to rationally design a custom-made AAV variant (AAV1-T593K; AAV1*) that exhibits improved transduction efficiencies in vitro and in vivo (14- and five-fold, respectively). Together, we present unique culturing method, transcriptomic profiles of quail's brain cells and a custom-tailored AAV1 for transduction of quail neurons in vitro and in vivo.

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.

Neural oscillations are locked to birdsong rhythms in canaries

How vocal communication signals are represented in the cortex is a major challenge for behavioural neuroscience. Beyond a descriptive code, it is relevant to unveil the dynamical mechanism responsible for the neural representation of auditory stimuli. In this work, we report evidence of synchronous neural activity in nucleus HVC, a telencephalic area of canaries (Serinus canaria), in response to auditory playback of the bird's own song. The rhythmic features of canary song allowed us to show that this large-scale synchronization was locked to defined features of the behaviour. We recorded neural activity in a brain region where sensorimotor integration occurs, showing the presence of well-defined oscillations in the local field potentials, which are locked to song rhythm. We also show a correspondence between local field potentials, multiunit activity and single unit activity within the same brain region. Overall, our results show that the rhythmic features of the vocal behaviour are represented in a telencephalic region of canaries.

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.

Miniature microscopes for manipulating and recording in vivo brain activity

Here we describe the development and application of miniature integrated microscopes (miniscopes) paired with microendoscopes that allow for the visualization and manipulation of neural circuits in superficial and subcortical brain regions in freely behaving animals. Over the past decade the miniscope platform has expanded to include simultaneous optogenetic capabilities, electrically-tunable lenses that enable multi-plane imaging, color-corrected optics, and an integrated data acquisition platform that streamlines multimodal experiments. Miniscopes have given researchers an unprecedented ability to monitor hundreds to thousands of genetically-defined neurons from weeks to months in both healthy and diseased animal brains. Sophisticated algorithms that take advantage of constrained matrix factorization allow for background estimation and reliable cell identification, greatly improving the reliability and scalability of source extraction for large imaging datasets. Data generated from miniscopes have empowered researchers to investigate the neural circuit underpinnings of a wide array of behaviors that cannot be studied under head-fixed conditions, such as sleep, reward seeking, learning and memory, social behaviors, and feeding. Importantly, the miniscope has broadened our understanding of how neural circuits can go awry in animal models of progressive neurological disorders, such as Parkinson's disease. Continued miniscope development, including the ability to record from multiple populations of cells simultaneously, along with continued multimodal integration of techniques such as electrophysiology, will allow for deeper understanding into the neural circuits that underlie complex and naturalistic behavior.

Circuit Investigation of Social Interaction and Substance Use Disorder Using Miniscopes

Substance use disorder (SUD) is comorbid with devastating health issues, social withdrawal, and isolation. Successful clinical treatments for SUD have used social interventions. Neurons can encode drug cues, and drug cues can trigger relapse. It is important to study how the activity in circuits and embedded cell types that encode drug cues develop in SUD. Exploring shared neurobiology between social interaction (SI) and SUD may explain why humans with access to social treatments still experience relapse. However, circuitry remains poorly characterized due to technical challenges in studying the complicated nature of SI and SUD. To understand the neural correlates of SI and SUD, it is important to: (1) identify cell types and circuits associated with SI and SUD, (2) record and manipulate neural activity encoding drug and social rewards over time, (3) monitor unrestrained animal behavior that allows reliable drug self-administration (SA) and SI. Miniaturized fluorescence microscopes (miniscopes) are ideally suited to meet these requirements. They can be used with gradient index (GRIN) lenses to image from deep brain structures implicated in SUD. Miniscopes can be combined with genetically encoded reporters to extract cell-type specific information. In this mini-review, we explore how miniscopes can be leveraged to uncover neural components of SI and SUD and advance potential therapeutic interventions.

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.

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.

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.

MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline

In vivo calcium imaging using a 1-photon-based miniscope and a microendoscopic lens enables studies of neural activities in freely behaving animals. However, the high and fluctuating background, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals emitted from imaged neurons inherent in this 1-photon imaging method present major challenges for extracting neuronal signals reliably and automatically from the raw imaging data. Here, we develop a unifying algorithm called the miniscope 1-photon imaging pipeline (MIN1PIPE), which contains several stand-alone modules and can handle a wide range of imaging conditions and qualities with minimal parameter tuning and automatically and accurately isolate spatially localized neural signals. We have quantitatively compared MIN1PIPE with other existing partial methods using both synthetic and real datasets obtained from different animal models and show that MIN1PIPE has superior efficiency and precision in analyzing noisy miniscope calcium imaging data.

Robust dynamical invariants in sequential neural activity

By studying different sources of temporal variability in central pattern generator (CPG) circuits, we unveil fundamental aspects of the instantaneous balance between flexibility and robustness in sequential dynamics -a property that characterizes many systems that display neural rhythms. Our analysis of the triphasic rhythm of the pyloric CPG (Carcinus maenas) shows strong robustness of transient dynamics in keeping not only the activation sequences but also specific cycle-by-cycle temporal relationships in the form of strong linear correlations between pivotal time intervals, i.e. dynamical invariants. The level of variability and coordination was characterized using intrinsic time references and intervals in long recordings of both regular and irregular rhythms. Out of the many possible combinations of time intervals studied, only two cycle-by-cycle dynamical invariants were identified, existing even outside steady states. While executing a neural sequence, dynamical invariants reflect constraints to optimize functionality by shaping the actual intervals in which activity emerges to build the sequence. Our results indicate that such boundaries to the adaptability arise from the interaction between the rich dynamics of neurons and connections. We suggest that invariant temporal sequence relationships could be present in other networks, including those shaping sequences of functional brain rhythms, and underlie rhythm programming and functionality.

Auditory-Motor Matching in Vocal Recognition and Imitative Learning

Songbirds possess mirror neurons (MNs) activating during the perception and execution of specific features of songs. These neurons are located in high vocal center (HVC), a premotor nucleus implicated in song perception, production and learning, making worth to inquire their properties and functions in vocal recognition and imitative learning. By integrating a body of brain and behavioral data, we discuss neurophysiology, anatomical, computational properties and possible functions of songbird MNs. We state that the neurophysiological properties of songbird MNs depends on sensorimotor regions that are outside the auditory neural system. Interestingly, songbirds MNs can be the result of the specific type of song representation possessed by some songbird species. At the functional level, we discuss whether songbird MNs are involved in others' song recognition, by dissecting the function of recognition in various different but possible overlapping processes: action-oriented perception, discriminative-oriented perception and identification of the signaler. We conclude that songbird MNs may be involved in recognizing other singer's vocalizations, while their role in imitative learning still require to solve how auditory feedback are used to correct own vocal performance to match the tutor song. Finally, we compare songbird and human mirror responses, hypothesizing a case of convergent evolution, and proposing new experimental directions.

Transitioning between preparatory and precisely sequenced neuronal activity in production of a skilled behavior

Precise neural sequences are associated with the production of well-learned skilled behaviors. Yet, how neural sequences arise in the brain remains unclear. In songbirds, premotor projection neurons in the cortical song nucleus HVC are necessary for producing learned song and exhibit precise sequential activity during singing. Using cell-type specific calcium imaging we identify populations of HVC premotor neurons associated with the beginning and ending of singing-related neural sequences. We characterize neurons that bookend singing-related sequences and neuronal populations that transition from sparse preparatory activity prior to song to precise neural sequences during singing. Recordings from downstream premotor neurons or the respiratory system suggest that pre-song activity may be involved in motor preparation to sing. These findings reveal population mechanisms associated with moving from non-vocal to vocal behavioral states and suggest that precise neural sequences begin and end as part of orchestrated activity across functionally diverse populations of cortical premotor neurons.

Observable for a Large System of Globally Coupled Excitable Units

The study of large arrays of coupled excitable systems has largely benefited from a technique proposed by Ott and Antonsen, which results in a low dimensional system of equations for the system’s order parameter. In this work, we show how to explicitly introduce a variable describing the global synaptic activation of the network into these family of models. This global variable is built by adding realistic synaptic time traces. We propose that this variable can, under certain conditions, be a good proxy for the local field potential of the network. We report experimental, in vivo, electrophysiology data supporting this claim.

Circuit Investigations With Open-Source Miniaturized Microscopes: Past, Present and Future

The ability to simultaneously image the spatiotemporal activity signatures from many neurons during unrestrained vertebrate behaviors has become possible through the development of miniaturized fluorescence microscopes, or miniscopes, sufficiently light to be carried by small animals such as bats, birds and rodents. Miniscopes have permitted the study of circuits underlying song vocalization, action sequencing, head-direction tuning, spatial memory encoding and sleep to name a few. The foundation for these microscopes has been laid over the last two decades through academic research with some of this work resulting in commercialization. More recently, open-source initiatives have led to an even broader adoption of miniscopes in the neuroscience community. Open-source designs allow for rapid modification and extension of their function, which has resulted in a new generation of miniscopes that now permit wire-free or wireless recording, concurrent electrophysiology and imaging, two-color fluorescence detection, simultaneous optical actuation and read-out as well as wide-field and volumetric light-field imaging. These novel miniscopes will further expand the toolset of those seeking affordable methods to probe neural circuit function during naturalistic behaviors. Here, we will discuss the early development, present use and future potential of miniscopes.

Leveraging calcium imaging to illuminate circuit dysfunction in addiction

Alcohol and drug use can dysregulate neural circuit function to produce a wide range of neuropsychiatric disorders, including addiction. To understand the neural circuit computations that mediate behavior, and how substances of abuse may transform them, we must first be able to observe the activity of circuits. While many techniques have been utilized to measure activity in specific brain regions, these regions are made up of heterogeneous sub-populations, and assessing activity from neuronal populations of interest has been an ongoing challenge. To fully understand how neural circuits mediate addiction-related behavior, we must be able to reveal the cellular granularity within brain regions and circuits by overlaying functional information with the genetic and anatomical identity of the cells involved. The development of genetically encoded calcium indicators, which can be targeted to populations of interest, allows for in vivo visualization of calcium dynamics, a proxy for neuronal activity, thus providing an avenue for real-time assessment of activity in genetically and anatomically defined populations during behavior. Here, we highlight recent advances in calcium imaging technology, compare the current technology with other state-of-the-art approaches for in vivo monitoring of neural activity, and discuss the strengths, limitations, and practical concerns for observing neural circuit activity in preclinical addiction models.

Excitatory and inhibitory synapse reorganization immediately after critical sensory experience in a vocal learner

Excitatory and inhibitory synapses are the brain’s most abundant synapse types. However, little is known about their formation during critical periods of motor skill learning, when sensory experience defines a motor target that animals strive to imitate. In songbirds, we find that exposure to tutor song leads to elimination of excitatory synapses in HVC (used here as a proper name), a key song generating brain area. A similar pruning is associated with song maturation, because juvenile birds have fewer excitatory synapses, the better their song imitations. In contrast, tutoring is associated with rapid insertion of inhibitory synapses, but the tutoring-induced structural imbalance between excitation and inhibition is eliminated during subsequent song maturation. Our work suggests that sensory exposure triggers the developmental onset of goal-specific motor circuits by increasing the relative strength of inhibition and it suggests a synapse-elimination model of song memorization.

A wide range of species use complex sounds to communicate, including humans and songbirds like zebra finches. During a critical period of learning, infants and young animals learn how to remember and discriminate this ‘language’ from other sounds. However, the changes that happen in the brain during this learning period are not well understood.

The process of learning forms new connections between neurons in the brain and prunes away old connections. These connections, known as synapses, come in different types. Signals sent across excitatory synapses increase the activity of the receiving neuron, while signals sent across inhibitory synapses reduce neuron activity.

What happens to the synapses in the brain during the critical period? To find out, Huang et al. used electron microscopy to examine the brains of young zebra finches that either had never heard birdsong, or had just heard birdsong for the first time. A single day of hearing song dramatically shifted the balance of excitatory and inhibitory synapses in the main vocal control area of the young birds’ brains. The number of excitatory synapses decreased, and the number of inhibitory synapses increased.

The balance between excitation and inhibition is important for the brain to work correctly. Therefore, as well as helping us to understand how infants learn their first language, the results presented by Huang et al. could also help us to improve treatments for conditions where this balance goes wrong, such as mood disorders. For example, tailoring the time point of medication intake in combination with sensory exposure therapies could improve how effectively either one works.

Quantifying syringeal dynamics in vitro using electroglottography

The complex and elaborate vocalizations uttered by many of the 10,000 extant bird species are considered a major driver in their evolutionary success, warranting study of the underlying mechanisms of vocal production. Additionally, birdsong has developed into a highly productive model system for vocal imitation learning and motor control, where, in contrast to humans, we have experimental access to the entire neuromechanical control loop. In human voice production, complex laryngeal geometry, vocal fold tissue properties, airflow and laryngeal musculature all interact to ultimately control vocal fold kinematics. Quantifying vocal fold kinematics is thus critical to understanding neuromechanical control of voiced sound production, but in vivo imaging of vocal fold kinematics in birds is experimentally challenging. Here, we adapted and tested electroglottography (EGG) as a novel tool for examining vocal fold kinematics in the avian vocal organ, the syrinx. We furthermore imaged and quantified syringeal kinematics in the pigeon (Columba livia) syrinx with unprecedented detail. Our results show that EGG signals predict (1) the relative amount of contact between the avian equivalent of vocal folds and (2) essential parameters describing vibratory kinematics, such as fundamental frequency, and timing of syringeal opening and closing events. As such, EGG provides novel opportunities for measuring syringeal vibratory kinematic parameters in vivo Furthermore, the opportunity for imaging syringeal vibratory kinematics from multiple planar views (horizontal and coronal) simultaneously promotes birds as an excellent model system for studying kinematics and control of voiced sound production in general, including in humans and other mammals.

An open source, wireless capable miniature microscope system

OBJECTIVE

Fluorescence imaging through head-mounted microscopes in freely behaving animals is becoming a standard method to study neural circuit function. Flexible, open-source designs are needed to spur evolution of the method.

APPROACH

We describe a miniature microscope for single-photon fluorescence imaging in freely behaving animals. The device is made from 3D printed parts and off-the-shelf components. These microscopes weigh less than 1.8 g, can be configured to image a variety of fluorophores, and can be used wirelessly or in conjunction with active commutators. Microscope control software, based in Swift for macOS, provides low-latency image processing capabilities for closed-loop, or BMI, experiments.

MAIN RESULTS

Miniature microscopes were deployed in the songbird premotor region HVC (used as a proper name), in singing zebra finches. Individual neurons yield temporally precise patterns of calcium activity that are consistent over repeated renditions of song. Several cells were tracked over timescales of weeks and months, providing an opportunity to study learning related changes in HVC.

SIGNIFICANCE

3D printed miniature microscopes, composed completely of consumer grade components, are a cost-effective, modular option for head-mounting imaging. These easily constructed and customizable tools provide access to cell-type specific neural ensembles over timescales of weeks.

Quantitative simulation of extracellular single unit recording from the surface of cortex

OBJECTIVE

Neural recording is important for a wide variety of clinical applications. Until recently, recording from the surface of the brain, even when using micro-electrocorticography (μECoG) arrays, was not thought to enable recording from individual neurons. Recent results suggest that when the surface electrode contact size is sufficiently small, it may be possible to record single neurons from the brain's surface. In this study, we use computational techniques to investigate the ability of surface electrodes to record the activity of single neurons.

APPROACH

The computational model included the rat head, μECoG electrode, two existing multi-compartmental neuron models, and a novel multi-compartmental neuron model derived from patch clamp experiments in layer 1 of the cortex.

MAIN RESULTS

Using these models, we reproduced single neuron recordings from μECoG arrays, and elucidated their possible source. The model resembles the experimental data when spikes originate from layer 1 neurons that are less than 60 μm from the cortical surface. We further used the model to explore the design space for surface electrodes. Although this model does not include biological or thermal noise, the results indicate the electrode contact area should be 100 μm² or smaller to maintain a detectable waveform amplitude. Furthermore, the model shows the width of lateral insulation could be reduced, which may reduce scar formation, while retaining 95% of signal amplitude.

SIGNIFICANCE

Overall, the model suggests single-unit surface recording is limited to neurons in layer 1 and further improvement in electrode design is needed.

Stable Sequential Activity Underlying the Maintenance of a Precisely Executed Skilled Behavior

A vast array of motor skills can be maintained throughout life. Do these behaviors require stability of individual neuron tuning or can the output of a given circuit remain constant despite fluctuations in single cells? This question is difficult to address due to the variability inherent in most motor actions studied in the laboratory. A notable exception, however, is the courtship song of the adult zebra finch, which is a learned, highly precise motor act mediated by orderly dynamics within premotor neurons of the forebrain. By longitudinally tracking the activity of excitatory projection neurons during singing using two-photon calcium imaging, we find that both the number and the precise timing of song-related spiking events remain nearly identical over the span of several weeks to months. These findings demonstrate that learned, complex behaviors can be stabilized by maintaining precise and invariant tuning at the level of single neurons.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

Building a state space for song learning

The songbird system has shed light on how the brain produces precisely timed behavioral sequences, and how the brain implements reinforcement learning (RL). RL is a powerful strategy for learning what action to produce in each state, but requires a unique representation of the states involved in the task. Songbird RL circuitry is thought to operate using a representation of each moment within song syllables, consistent with the sparse sequential bursting of neurons in premotor cortical nucleus HVC. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Here, we review and expand upon a model for how the songbird brain could construct latent sequences to support RL, in light of new data elucidating connections between HVC and auditory cortical areas. We hypothesize that learning occurs via four distinct plasticity processes: 1) formation of 'tutor memory' sequences in auditory areas; 2) formation of appropriately-timed latent HVC sequences, seeded by inputs from auditory areas spontaneously replaying the tutor song; 3) strengthening, during spontaneous replay, of connections from HVC to auditory neurons of corresponding timing in the 'tutor memory' sequence, aligning auditory and motor representations for subsequent song evaluation; and 4) strengthening of connections from premotor neurons to motor output neurons that produce the desired sounds, via well-described song RL circuitry.

Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch

The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise time points during song production. However, the neural representation of this behavior at a population level remains elusive. We used two-photon microscopy to monitor ensemble activity during singing, integrating across multiple trials by adopting a Bayesian inference approach to more precisely estimate burst timing. Additionally, we examined spiking and motor-related synaptic inputs using intracellular recordings during singing. With both experimental approaches, we find that premotor events do not occur preferentially at the onsets or offsets of song syllables or at specific subsyllabic motor landmarks. These results strongly support the notion that HVC projection neurons collectively exhibit a temporal sequence during singing that is uncoupled from ongoing movements.

Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex

Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.

EM connectomics reveals axonal target variation in a sequence-generating network

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.

Advantages of comparative studies in songbirds to understand the neural basis of sensorimotor integration

Sensorimotor integration is the process through which the nervous system creates a link between motor commands and associated sensory feedback. This process allows for the acquisition and refinement of many behaviors, including learned communication behaviors such as speech and birdsong. Consequently, it is important to understand fundamental mechanisms of sensorimotor integration, and comparative analyses of this process can provide vital insight. Songbirds offer a powerful comparative model system to study how the nervous system links motor and sensory information for learning and control. This is because the acquisition, maintenance, and control of birdsong critically depend on sensory feedback. Furthermore, there is an incredible diversity of song organizations across songbird species, ranging from songs with simple, stereotyped sequences to songs with complex sequencing of vocal gestures, as well as a wide diversity of song repertoire sizes. Despite this diversity, the neural circuitry for song learning, control, and maintenance remains highly similar across species. Here, we highlight the utility of songbirds for the analysis of sensorimotor integration and the insights about mechanisms of sensorimotor integration gained by comparing different songbird species. Key conclusions from this comparative analysis are that variation in song sequence complexity seems to covary with the strength of feedback signals in sensorimotor circuits and that sensorimotor circuits contain distinct representations of elements in the vocal repertoire, possibly enabling evolutionary variation in repertoire sizes. We conclude our review by highlighting important areas of research that could benefit from increased comparative focus, with particular emphasis on the integration of new technologies.

Brains for birds and babies: Neural parallels between birdsong and speech acquisition

Language as a computational cognitive mechanism appears to be unique to the human species. However, there are remarkable behavioral similarities between song learning in songbirds and speech acquisition in human infants that are absent in non-human primates. Here we review important neural parallels between birdsong and speech. In both cases there are separate but continually interacting neural networks that underlie vocal production, sensorimotor learning, and auditory perception and memory. As in the case of human speech, neural activity related to birdsong learning is lateralized, and mirror neurons linking perception and performance may contribute to sensorimotor learning. In songbirds that are learning their songs, there is continual interaction between secondary auditory regions and sensorimotor regions, similar to the interaction between Wernicke's and Broca's areas in human infants acquiring speech and language. Taken together, song learning in birds and speech acquisition in humans may provide useful insights into the evolution and mechanisms of auditory-vocal learning.

Unstable neurons underlie a stable learned behavior

Motor skills can be maintained for decades, but the biological basis of this memory persistence remains largely unknown. The zebra finch, for example, sings a highly stereotyped song that is stable for years, but it is not known whether the precise neural patterns underlying song are stable or shift from day to day. Here we demonstrate that the population of projection neurons coding for song in the premotor nucleus, HVC, change from day to day. The most dramatic shifts occur over intervals of sleep. In contrast to the transient participation of excitatory neurons, ensemble measurements dominated by inhibition persist unchanged even after damage to downstream motor nerves. These observations offer a principle of motor stability: spatiotemporal patterns of inhibition can maintain a stable scaffold for motor dynamics while the population of principal neurons that directly drive behavior shift from one day to the next.

A Neural Code That Is Isometric to Vocal Output and Correlates with Its Sensory Consequences

What cortical inputs are provided to motor control areas while they drive complex learned behaviors? We study this question in the nucleus interface of the nidopallium (NIf), which is required for normal birdsong production and provides the main source of auditory input to HVC, the driver of adult song. In juvenile and adult zebra finches, we find that spikes in NIf projection neurons precede vocalizations by several tens of milliseconds and are insensitive to distortions of auditory feedback. We identify a local isometry between NIf output and vocalizations: quasi-identical notes produced in different syllables are preceded by highly similar NIf spike patterns. NIf multiunit firing during song precedes responses in auditory cortical neurons by about 50 ms, revealing delayed congruence between NIf spiking and a neural representation of auditory feedback. Our findings suggest that NIf codes for imminent acoustic events within vocal performance.

Transmission of birdsong across generations requires tight interactions between auditory and vocal systems. However, how these interactions take place is poorly understood. We studied neuronal activity in the brain area located at the intersection between auditory and song motor areas, which is known as the nucleus interface of the nidopallium. By recording during singing from neurons in the nucleus interface of the nidopallium that project to motor areas, we found that their spiking precedes peaks in vocal amplitudes by about 50 ms. Notably, quasi-identical notes produced at different times in the song motif were preceded by highly similar spike patterns in these projection neurons. Such local isometry between output from the nucleus interface of the nidopallium and vocalizations suggests that projection neurons in this brain area code for imminent acoustic events within vocal performance. In support of this conclusion, during singing, projection neurons do not respond to playback of white noise sound stimuli, and activity in the nucleus interface of the nidopallium precedes by about 50 ms neural activity in the avian analogue of auditory cortex. Therefore, we conclude that the role of neuronal activity in the nucleus interface of the nidopallium could be to link desired auditory targets to suitable motor commands required for hitting these targets.

Synchronized excitability in a network enables generation of internal neuronal sequences

Hippocampal place field sequences are supported by sensory cues and network internal mechanisms. In contrast, sharp-wave (SPW) sequences, theta sequences, and episode field sequences are internally generated. The relationship of these sequences to memory is unclear. SPW sequences have been shown to support learning and have been assumed to also support episodic memory. Conversely, we demonstrate these SPW sequences were present in trained rats even after episodic memory was impaired and after other internal sequences - episode field and theta sequences - were eliminated. SPW sequences did not support memory despite continuing to 'replay' all task-related sequences - place- field and episode field sequences. Sequence replay occurred selectively during synchronous increases of population excitability -- SPWs. Similarly, theta sequences depended on the presence of repeated synchronized waves of excitability - theta oscillations. Thus, we suggest that either intermittent or rhythmic synchronized changes of excitability trigger sequential firing of neurons, which in turn supports learning and/or memory.

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.

Insights into the Neural and Genetic Basis of Vocal Communication

The use of vocalizations to communicate information and elaborate social bonds is an adaptation seen in many vertebrate species. Human speech is an extreme version of this pervasive form of communication. Unlike the vocalizations exhibited by the majority of land vertebrates, speech is a learned behavior requiring early sensory exposure and auditory feedback for its development and maintenance. Studies in humans and a small number of other species have provided insights into the neural and genetic basis for learned vocal communication and are helping to delineate the roles of brain circuits across the cortex, basal ganglia, and cerebellum in generating vocal behaviors. This Review provides an outline of the current knowledge about these circuits and the genes implicated in vocal communication, as well as a perspective on future research directions in this field.

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.

Visualization of cortical, subcortical and deep brain neural circuit dynamics during naturalistic mammalian behavior with head-mounted microscopes and chronically implanted lenses

Genetically encoded calcium indicators for visualizing dynamic cellular activity have greatly expanded our understanding of the brain. However, owing to the light-scattering properties of the brain, as well as the size and rigidity of traditional imaging technology, in vivo calcium imaging has been limited to superficial brain structures during head-fixed behavioral tasks. These limitations can now be circumvented by using miniature, integrated microscopes in conjunction with an implantable microendoscopic lens to guide light into and out of the brain, thus permitting optical access to deep brain (or superficial) neural ensembles during naturalistic behaviors. Here we describe steps to conduct such imaging studies using mice. However, we anticipate that the protocol can be easily adapted for use in other small vertebrates. Successful completion of this protocol will permit cellular imaging of neuronal activity and the generation of data sets with sufficient statistical power to correlate neural activity with stimulus presentation, physiological state and other aspects of complex behavioral tasks. This protocol takes 6-11 weeks to complete.

An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging

Advances in neurotechnology have been integral to the investigation of neural circuit function in systems neuroscience. Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. While exciting technical advances demonstrate the potential of this technique, further improvement in data acquisition and analysis, especially those that allow effective processing of increasingly larger datasets, would greatly promote the application of optical imaging in systems neuroscience. Here we demonstrate the ability of wide-field imaging to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. This system allows the visualization of morphological details at a higher spatial resolution than has been previously achieved using similar functional imaging modalities. To analyze the expansive data sets, we developed software to facilitate rapid downstream data processing. Using this system, we show that a large fraction of anatomically distinct hippocampal neurons respond to discrete environmental stimuli associated with classical conditioning, and that the observed temporal dynamics of transient calcium signals are sufficient for exploring certain spatiotemporal features of large neural networks.

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.