Marc F. Schmidt, PhD - US grants

DESCRIPTION: This proposal is aimed at characterizing the pathway for -1-In the first aim, recordings will be used to characterize response patterns in the auditory pathway. In the second aim chronic recordings will be used to track changes in response patterns from the beginnings of song development in the young to song crystallization in the adult. In adult birds, auditory feedback is inhibited in the nucleus HVc. This proposal will test the hypothesis that little or no inhibition will be found in the HVc in young birds prior to song crystallization. During the critical periods of template matching and song development, it is proposed that such inhibition would interfere with the necessary flow of feedback through the HVc to the anterior forebrain loop. Adult levels of inhibition in the HVc are expected only after songs have crystallized into adult patterns. Preliminary records from chronic implants in free-flying adults have been made in Field L, the source of auditory input to the HVc.

DESCRIPTION (provided by applicant): With the exception of relatively simple rhythmic motor patterns, models of motor control often place most of their emphasis on the telencephalon. This is certainly true for the production of learned vocal behaviors where the brainstem is viewed primarily as an output pathway for motor commands generated in the forebrain. With the recent exception of the motor control of eye movement, little is known about how brainstem nuclei involved in motor output might directly influence the generation of motor commands in the telencephalon. The experiments described in this proposal are aimed at elucidating the role of the respiratory brainstem in song pattern generation and more generally how the brainstem influences motor command generations in the forebrain. We focus our efforts on two nuclei, PAm and DM, which form a critical link in the recurrent song system network. They each receive direct input from the descending motor pathway and projecting bilaterally back to HVC, a key forebrain vocal control nucleus, via the intermediary of the thalamic relay nucleus Uva. We hypothesize that these nuclei play a critical role in synchronizing premotor activity in both hemispheres during key transitions in the song. These experiments are important because Uva lesions completely disrupt song behavior and because of our near complete lack of understanding of the functional role of the vocal-respiratory brainstem in song pattern generation. In the first aim, we propose to characterize neural properties in PAm and DM and evaluate their functional relationship to neurons in their primary input and output structures. We will perform these experiments in restrained sleeping birds. This allows a greater range of experimental accessibility, such as paired recordings. We will use several experimental paradigms, including stimulus-evoked vocalizations and song presentation, that can drive the motor system in order to examine network properties under conditions that approach vocal production. In the second aim, we will record from single neurons in both PAm and DM in awake vocalizing birds to gain insight into their firing properties during vocal production. We will carefully quantify the relationship between activity patterns and vocal output. In the final aim, we will investigate the relationship, during vocal production, between input-output networks of neurons in the vocal-respiratory brainstem. We will take advantage of the variability in song output across renditions to inform us about the role of each of these different structures in song pattern generation. Understanding the emerging principles of brainstem-to-forebrain motor signaling and the role the respiratory system might play in vocal control will provide insight into the neural control of complex motor behavior including human speech production and its pathologies PUBLIC HEALTH RELEVANCE Brainstem respiratory networks are not well integrated within the context of vocal production because they are often viewed exclusively as output structures. In this proposal, we aim to investigate the nature of brainstem-to-forebrain motor signaling and the role the respiratory system plays in vocal control. This work will provide insight into the neural control of complex motor behavior, including human speech production and its pathologies.

DESCRIPTION (provided by applicant): We propose to investigate the complex relationship between behavioral state and sensory processing in a high-order sensory-motor area. In the avian song system, nucleus HVC is a key forebrain nucleus that links auditory and vocal motor pathways. Consistent with a role in auditory processing, HVC neurons in sleeping or anesthetized songbirds respond robustly to the bird's own song (BOS). Surprisingly, in awake birds these same neurons respond quite variably and are no longer exclusively tuned to the BOS. Thus auditory responses at one recording site can vary over a few hours from complete suppression to robust. Because arousal selectively suppresses HVC auditory responses without affecting the auditory forebrain structure Field L, the avian analogue of the auditory cortex, we hypothesize that auditory responses in HVC, or auditory structures directly afferent to it, are modulated by changes in behavioral state. We will investigate the mechanism for these modulations in the adult zebra finch (Taeniopygia guttata). Our goal is to understand both the behavioral conditions that modulate auditory responses and the mechanism. In AIM 1, we propose to use our ability to suppress auditory responses in HVC by brief arousal of lightly sedated birds to identify the auditory structure afferent to HVC where arousal first suppresses auditory responses. Having identified this structure, we will then test the mechanism of arousal-mediated suppression of auditory activity by recording from HVC while pharmacologically manipulating neuromodulatory systems in the target structure identified above. In AIM 2, we use a song discrimination task to investigate whether auditory responses in HVC are modulated by attention. We hypothesize that attention will enhance auditory responses in HVC. In AIM 3, we propose to record HVC activity while the bird hears and interacts with conspecific males in his colony. We hypothesize that the behavioral context with which the heard song is produced as well as the identity of the singer will significantly influence auditory responses.

This project, developing a new instrument to enable an accurate quantitative analysis of the movement of animals and vocal expressions in real world scenes, aims to facilitate innovative research in the study of animal behavior and neuroscience in complex realistic environments. While much progress has been made investigating brain mechanisms of behavior, these have been limited primarily to studying individual subjects in relatively simple settings. For many social species, including humans, understanding neurobiological processes within the confines of these more complex environments is critical because their brains have evolved to perceive and evaluate signals within a social context. Indeed, today's advances in video capture hardware and storage and in algorithms in computer vision and network science make this facilitation with animals possible. Past work has relied on subjective and time-consuming observations from video streams, which suffer from imprecision, low dimensionality, and the limitations of the expert analyst's sensory discriminability. This instrument will not only automate the process of detecting behaviors but also provide an exact numeric characterization in time and space for each individual in the social group. While not explicitly part of the instrument, the quantitative description provided by our system will allow the ability to correlate social context with neural measurements, a task that may only be accomplished when sufficient spatiotemporal precision has been achieved.

The instrument enables research in the behavioral and neural sciences and development of novel algorithms in computer vision and network theory. In the behavioral sciences, the instrumentation allows the generation of network models of social behavior in small groups of animals or humans that can be used to ask questions that can range from how the dynamics of the networks influence sexual selection, reproductive success, and even health messaging to how vocal decision making in individuals gives rise to social dominance hierarchies. In the neural sciences, the precise spatio-temporal information the system would provide can be used to evaluate the neural bases of sensory processing and behavioral decision under precisely defined social contexts. Sensory responses to a given vocal stimulus, for example, can be evaluated by the context in which the animal heard the stimulus and both his and the sender's prior behavioral history in the group. In computer vision, we propose novel approaches for the calibration of multiple cameras "in the wild", the combination of appearance and geometry for the extraction of exact 3D pose and body parts from video, the learning of attentional focus among animals in a group, and the estimation of sound source and the classification of vocalizations. New approaches will be used on hierarchical discovery of behaviors in graphs, the incorporation of interactions beyond the pairwise level with simplicial complices, and a novel theory of graph dynamics for the temporal evolution of social behavior. The instrumentation benefits behavioral and neural scientists. Therefore, the code and algorithms developed will be open-source so that the scientific community can extend them based on the application. The proposed work also impacts computer vision and network science because the fundamental algorithms designed should advance the state of the art. For performance evaluation of other computer vision algorithms, established datasets will be employed.

For many decades, neuroscientists and evolutionary biologists have been interested in the mechanics and function of the songbird's "song system": the interconnected neural circuit that connects the higher-order auditory areas in the brain with the motor circuits in the brainstem that drive behavior. This work predominantly has focused on how the song system allows male songbirds to learn and produce song. The role of this circuit in female songbirds, which do not sing, has largely been ignored. Rather than acting as a circuit that generates vocal behavior, this work investigates the hypothesis that the "song system" in females serves to organize preferences for males' songs and guides their behavioral reactions to song in the form of a copulation solicitation display that ensures survival of the species. The project capitalize on the robustness, selectivity, and social malleability of the copulatory behavior in the brown-headed cowbird, to investigate how the song system transforms a sensory stimulus (the song) into a motor command that controls a postural response. The project also provides opportunities for undergraduate and graduate students to engage in interdisciplinary research, and it includes science education activities aimed at elementary school children as well as a comprehensive summer course in neuroscience for high school students.

The proposed work integrates disparate fields of science, including neuroscience, behavior, and engineering to provide unique insight into the evolution of neural circuits that control behavior. In the first aim, the investigators use a combination of classic pathway tracing techniques and recently developed transsynaptic tracer (vesicular stomatitis virus) to map the connectivity from the forebrain to the individual muscle groups that are activated during the production of a copulation solicitation display (CSD). In the second aim, the investigators record neural activity in forebrain song control nuclei HVC and RA during the production of CSD in female cowbirds to quantify the nature of the forebrain motor commands that control this highly selective sexual behavior. To evaluate the relationship between recorded neural activity patterns and the behavior, we will use a computer vision approach to quantify the copulatory behavior. In the final aim of the proposal, the investigators record neural responses to song in higher-order auditory forebrain areas (NCM, NIf, CM) within the context of CSD production. These experiments serve to test the hypothesis that these forebrain areas, which have known projections to song control nuclei, encode song valence and provide a direct link between song quality and the females' behavioral response. The neural and behavioral data will be made available at public internet site dedicated to Song Bird Science.

Advances in technology, mathematics, computing and engineering are making it possible to quantify behaviors within complex naturalistic environments and to relate them to underlying neural mechanisms. For social animals, which have evolved to perceive and evaluate signals within a community context, the ability to link neural function with the precise social environment is especially important and challenging. Little is currently known about how the brain integrates complex social information and how such information might be encoded. This stems in part from the experimental challenge of measuring and assessing the variables that determine a social context and then linking the state of a social network to precise neural events. This project has assembled an interdisciplinary team of engineers, neurobiologists and computational scientists to create a platform to record and evaluate brain dynamics in individual animals navigating a complex social environment. In addition to the challenge and opportunity of using sophisticated engineering and computational approaches to study how brains encode social information, this work will generate a complex dataset that will offer unique opportunities for developing novel mathematical methods to quantify and visualize social networks that can be applied to other disciplines.

In this study, a "smart aviary" is equipped with arrays of cameras and microphones to create a fully automated system for tracking moment-to-moment behavioral events for each individual songbird within a social group. The songbirds are of a highly gregarious species (brown-headed cowbird, Molothrus ater) that uses vocal communications to form and maintain a complex social system. As a variable, social context needs to be mathematically constructed over multiple timescales from the sequence of all behavioral events. This entails the development of new mathematical approaches and statistical models for quantifying social network state so that individual neural events can be linked back to the precise social contexts. The project will develop new machine learning approaches for automated capture of social interactions over months-long time periods. In addition, an articulated mesh model enables visual signals to be captured in unprecedented detail, allowing the quantification of shape-mediated social signaling.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.