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Understanding how neural circuits control complex social and spatial behaviors in humans requires a tractable mammalian model system that allows one to probe both the appropriate behavior and its underlying neural activity. Egyptian fruit bats (Rousettus aegyptiacus) are an attractive model for studying the motor control of vocal communication and navigation. To thrive within hierarchical social structures in dark cave environments, bats evolved specialized auditory and vocal production systems to both echolocate and communicate with one another. However, to date, the detailed neural computations that support this behavior in mammals are unknown. Furthermore, in the wild, bats fly highly reproducible trajectories from their home cave to foraging sites over 20km round trip nightly. It is unclear whether the neural representation of these flight paths remains stable across days when a stable behavior is performed. In this thesis, I describe my efforts to study the behavior, anatomy, and neurophysiology of vocal communication and spatial navigation in a novel model organism.In Chapter 1, I provide and introduction to theories of vocal learning, mammalian motor control of vocal production, and social communication. Furthermore, I provide a focused review of the hippocampal representation of place and current theories of how this representation changes across days. In Chapter 2, I present my efforts to design a behavioral task that operantly trained the bats to produce vocalizations for reward. Additionally, I present anatomical tracing data that identified the motor cortical site of laryngeal control and supports the theory of vocal learning in bats. Finally, I discuss collaborations to probe the underlying electrophysiology in this motor region during the vocal production task. In Chapter 3, I present my work developing a wireless miniature fluorescent microscope and its use to study the longitudinal stability of spatial coding in freely flying bats performing a self-paced navigation task. This work is presented as a manuscript (currently under review) with my co-author Dr. William Liberti.

Our interactions with the world unfold over time. Whether it's speaking, where one word follows the next, or walking, where each step follows another, the organization of our behaviors in time tends to follow a predictable pattern. Those patterns are dictated by a multitude of underlying factors, influenced both by endogenous physiological factors like the rhythmic nature of our gait as well as by exogenous factors, like the social dynamics underlying turn-taking while speaking. Despite decades of research studying the temporal organization of behavior, dating back to the work of influential biologists like Tinbergen, Lashley, and Dawkins, little is known about the physiological substrates that underlie either the production of the sequential organization of most aspects of behavior. Despite widespread acknowledgment that physiological motor programs and many non-linguistic behaviors are hierarchical, for example, few physiological investigations into the dynamics of behavior extend beyond low-order (Markovian) transition statistics. In this thesis, I build onto the emerging field of computational neuroethology to further our understanding of what structure underlies the sequential organization of behavior, what physiological mechanisms might be involved in producing, perceiving, and representing sequential behavioral organization, and how sequential behavioral organization might have emerged developmentally and evolutionarily. Throughout the thesis, I draw primarily upon birdsong and human speech, developing methods to analyze the acoustic and temporal structure in vocal signals and then behaviorally and physiologically probing the underpinnings of sequential organization in the songbird. This work advances the field of computational neuroethology in several ways.I uncover novel acoustic structure in vocal signals separating avian and mammalian vocalizations along a spectrum of vocal stereotypy. I observe that both human speech and birdsong are characterized by a combination of long and short-range temporal patterning.I find that the long-range temporal patterning characterizing human speech, believed to be underlied by hierarchical linguistic organization, is present at the earliest developmental stages of human speech, well before complex syntax is produced.I find that the perceptual integration of birdsong syllable sequences can be well explained by Bayesian models of probabilistic perceptual decision-making. Finally, I find that sensory neural representations of syllable sequences are modulated by sequential context and that this modulation reflects the animals underlying perceptual behavior. In the following paragraphs, I give a brief overview of the methods and major results of the chapters comprising this thesis.In Chapter \ref{chapter:review} I give an introduction to the emerging field of vocal computational neuroethology. This introduction contextualizes the following chapters in a review of current work. I emphasize current tools, challenges, and future directions in vocal neuroethology. I start with a discussion of low-level bioacoustics challenges and build up to a discussion of behavioral organization and physiology. I first discuss challenges in signal processing such as dealing with noise and signals and representing vocal signals as time-frequency representations. I then discuss machine learning approaches used to identify, segment, and label vocalizations. Next, I discuss how to extract relational structure between vocalizations, and cluster latent projections of vocalizations. I then give an overview of methods for capturing temporal relationships in vocal sequences, outlining traditional Markovian descriptions of vocal structure, and new tools for capturing long-range structure, enabled by large datasets. I then move on to machine learning tools that can be used to systematically control and synthesize vocal signals from learned vocal spaces. Finally, I discuss how these techniques are being utilized in several active areas of neuroethology research. In Chapter \ref{chapter:avgn} I develop a set of methods to visualize and quantify relational structure in vocalizations, which enable the analyses and experiments performed in the following chapters. I use graph-based dimensionality reduction to uncover local structure in vocal communication signals and apply that technique to 19 datasets consisting of vocalizations from 29 species, including songbirds, primates, cetaceans, rodents, and bats. I observe that these methods uncover novel structure in animal vocal signals, including vocal dialects, acoustic units, behaviorally relevant signal information, and sub-syllabic structure. In Chapter \ref{chapter:parametric_umap}, I extend the methods from Chapter \ref{chapter:avgn} by introducing Parametric UMAP, a graph-based dimensionality reduction algorithm that parametrically learns the relationship between data (here vocal signals) and latent embeddings. Parametric UMAP enables the methods from Chapter \ref{chapter:avgn} to be applied in real-time closed-looped settings over larger datasets due to the learned parametric embeddings. I show that this algorithm has applications in semi-supervised settings, and provides additional control over the trade-off between capturing global and local structure in embeddings. In Chapter \ref{chapter:parallels} I explore the long and short-range temporal patterning of vocal sequences in birdsong and human speech. I use an information-theoretic framework to analyze statistical dependencies as a function of the distance between elements in vocal sequences. I find that both birdsong and human speech exhibit two forms of structure: short-range relationships captured by Markovian dynamics over short-timescales, and long-range relationships that follow a power-law occurring over longer timescales. In language, the observed short-range organization conforms to phonological processes, which are well-described by finite-state dynamics, while long-range organization suggests more complex dynamics such as underlying hierarchical organization. Previous analyses of birdsong have only identified short-range Markovian dynamics, making our observation of long-range dynamics in birdsong novel. In Chapter \ref{chapter:lri} I extend our experiment from chapter \ref{chapter:parallels} over human speech to language acquisition. By analyzing corpora of speech throughout language development, we can observe the time course of the emergence of long and short-range relationships over development. Surprisingly, I find that long-range statistical dependencies are present in children's speech as early as 6-12 months, well before complex syntactic structure is present. I discuss these results alongside emerging evidence from computational ethology that long-range relationships are also common to non-linguistic behavioral signals from animals as diverse as zebrafish, drosophila, and whales. Although previous analyses of long-range relationships have suggested that long-range relationships are the product of hierarchical linguistic structure such as syntax and discourse structure, our observations in developmental speech and non-linguistic behaviors suggest that other mechanisms may also be at play. Finally, in Chapter \ref{chapter:cdcp} I probe how sequential dependencies in vocal sequences are integrated behaviorally and physiologically. I developed a behavioral task in which European starlings are trained to classify morphs of syllables of starling song synthesized from an interpolation between two points in the latent space of a neural network (a Variational Autoencoder). These morph syllables are preceded with a separate syllable (a cue syllable), which holds predictive information about the category of the following morph syllable. I find that classification of the morph syllable is contextually modulated by the predictive probability of the cue syllable, which can be well explained by a model of Bayesian integration. With the same behavioral paradigm, I then record chronic electrophysiology data from auditory nuclei while birds performed this context-dependent categorical perceptual decision-making task. I find that neural activity patterns reflect several aspects of our model of perceptual behavior, including the uncertainty in decision making, and prediction-related perceptual modulation.

Our primate Order is known for the expansion of the neocortex relative to other mammals. This distinction is coupled with a characteristic complex society that is facilitated by dynamic social cognitive mechanisms and systems of communication. Because of this intricate relationship, investigating the neural basis of communication within primates affords the opportunity to better understand how different dimensions of sociality are supported by the structures of the brain itself. Much of the research on the neuroscience of communication in primates has hinged on studies of vocalization processing in head-restrained monkeys either passively listening to stimuli or engaged in a conditional behavioral task. But the information communicated by social signals are heavily influenced by the natural contexts they occur in, and auditory processing of vocalizations within the brain may likewise be heavily affected by the context in which conspecific vocalizations are heard; thus, the experiments may not fully capture the neural basis of communication. I hypothesize that the traditional experimental contexts typical of nonhuman primate neuroscience research has divorced the signal from its natural context, and, consequently, limited our understanding of how various neocortical structures support these processes. Here I sought to address this critical gap in our knowledge by implementing novel experimental paradigms designed to explicate the neurobiology and behavior of natural communication in freely-moving marmoset monkeys (Callithrix jacchus). In this dissertation, I detail the results of new insights gained from the innovative experiments that support my hypothesis. Chapter 1 shows how broad ‘states’ of neural populations in frontal cortex during natural, untrained behavior of antiphonal conversations in the marmoset predicts whether subjects respond to a conspecific call. Chapter 2 shows robust within-neuron differences in how prefrontal cortex neurons respond to vocalizations between traditional head-restrained contexts and natural behavior suggesting that data recorded in the former context is not predictive of the latter. Finally, Chapter 3 shows my novel multi-speaker paradigm that simulates the natural communication networks in marmosets (i.e. “Cocktail Party”) to study the vocal processing of marmosets in complex acoustic environments previously inaccessible to researchers for any other animal model. Results demonstrate that marmosets employ similar perceptual mechanisms as humans to communicate in these dynamic acoustic and social landscapes. These findings establish a novel paradigm in which to explore the neurobiology of primate communication in dynamic, multi-speaker communication networks that more closely resemble their natural communication systems.

While the auditory cortex is necessary for sound localization, we do not yet know the underlying mechanisms that lead to the behavior. To address this gap, we have designed and executed a series of experiments aimed at describing the neuroethology of sound localization in the pallid bat, an animal which relies on passive hearing of prey-generated noise when foraging. We have made three primary discoveries. First, we have discovered how the external ear filters incoming sounds before they reach the sensory epithelium. Low frequency spectral notches, important for the perception of sound source elevation, depend on the presence of the conspicuous tragus. Second, we developed a sound localization task to test the behavior of the pallid bat. Pallid bats demonstrate remarkable sound localization acuity of ~4° near the midline, and we show that the high frequency range of prey-generated noise (20-30 kHz) is the predominant bandwidth used for azimuth localization. Last, we describe the 2D spatial selectivity of auditory cortex neurons and propose a population code for 2D sound localization. Our data suggests that the extent of active cortex is constrained systematically by the horizontal and elevational sound source direction. We describe preliminary results that indicate the chemogenetic inhibitory DREADDs system is a viable method for answering questions about the necessity of these auditory cortex populations to sound localization behavior. Collectively, the research contained in this dissertation provides insight into the way the auditory system is able to compute representations of external space from sound filtered by the external ear and supports the notion of the pallid bat as a viable model from which basic understandings of mammalian sound localization can be obtained.

Song is a learned motor behavior where each vocal element (syllable) has its own distinct acoustic structure. Syllable features, like pitch and amplitude, have been linked to the activity of the forebrain nucleus RA; but whether these features are directly controlled by the level of RA activity remains to be shown. Natural variation in RA activity may be produced by a dynamic interaction between RA projection neurons and interneurons; but a role for local inhibition in RA for shaping vocal output is also unknown. Here, we address whether changing RA activity by modulating the influence of RA inhibition has a direct effect on syllable structure. We find that increasing or decreasing RA inhibition drives a corresponding increase and decrease in syllable pitch and amplitude. These results provide evidence that local inhibition within RA actively contributes to song production and suggest that maintaining pitch and amplitude at a desired level requires a careful balance of excitation and inhibition acting within RA. Song also has a specific temporal structure that is linked to activity in the forebrain nucleus HVC. Previous studies have found that manipulating the temporal dynamics of HVC activity drives changes to song timing and sequencing. This supports a hierarchical model of song control in which temporal and spectral properties of song (such as syllable sequencing and timing versus syllable pitch and amplitude) are separately controlled by HVC and RA. However, the downstream targets of RA in the brainstem also make ascending projections back to HVC, raising the possibility that the output of RA may recurrently influence HVC activity to shape song timing and sequencing. Here, we find that natural variation in RA activity is correlated with variation in song timing; specifically, higher firing in RA is associated with shorter durations of the gaps between syllables. We also find that increasing or decreasing the influence of RA inhibition drives bidirectional changes to song tempo and syllable repetitions. These high order song effects are not dependent on auditory feedback. Taken together, these findings indicate that RA activity contributes centrally to the temporal structure of song.

Leeches in the wild are often found on smooth surfaces, such as vegetation, smooth rocks or human artifacts such as bottles and cans, thus exhibiting what appears to be a “substrate texture preference behavior”. Here, we have reproduced this behavior under controlled circumstances, by allowing leeches to step about freely on a range of silicon carbide sandpaper substrates. To begin to understand the neural mechanisms underlying this texture preference behavior, we have determined relevant parameters of leech behavior both on uniform substrates of varying textures, and in a behavior choice paradigm in which the leech is confronted with a choice between rougher and smoother substrate textures at each step. We tested two non-exclusive mechanisms which could produce substrate texture preference: 1) a Diffusion Trap mechanism, in which a leech is more likely to stop moving on a smooth surface than on a rough one, and; 2) an Anterior Choice mechanism, in which a leech is more likely to attach its front sucker (prerequisite for taking a step) to a smooth surface than to a rough one. We propose that both mechanisms contribute to the texture preference exhibited by leeches.

Author(s): Coutinho-Abreu, Iliano V; Forster, Lisa; Guda, Tom; Ray, Anandasankar | Abstract: BackgroundThe Asian Citrus Psyllid (ACP), Diaphorina citri, can transmit the bacterium Candidatus Liberibacter while feeding on citrus flush shoots. This bacterium causes Huanglongbing (HLB), a major disease of citrus cultivation worldwide necessitating the development of new tools for ACP surveillance and control. The olfactory system of ACP is sensitive to variety of odorants released by citrus plants and offers an opportunity to develop new attractants and repellents.ResultsIn this study, we performed single-unit electrophysiology to identify odorants that are strong activators, inhibitors, and prolonged activators of ACP odorant receptor neurons (ORNs). We identified a suite of odorants that activated the ORNs with high specificity and sensitivity, which may be useful in eliciting behavior such as attraction. In separate experiments, we also identified odorants that evoked prolonged ORN responses and antagonistic odorants able to suppress neuronal responses to activators, both of which can be useful in lowering attraction to hosts. In field trials, we tested the electrophysiologically identified activating odorants and identified a 3-odor blend that enhances trap catches by ∼230%.ConclusionThese findings provide a set of odorants that can be used to develop affordable and safe odor-based surveillance and masking strategies for this dangerous pest insect.

Autonomic nervous system activity is an important component of affective experience. We demonstrate in the rhesus monkey that both the sympathetic and parasympathetic branches of the autonomic nervous system respond differentially to the affective valence of passively viewed video stimuli. We recorded cardiac impedance and an electrocardiogram while adult macaques watched a series of 300 30-second videos that varied in their affective content. We found that sympathetic activity (as measured by cardiac pre-ejection period) increased and parasympathetic activity (as measured by respiratory sinus arrhythmia) decreased as video content changes from positive to negative. These findings parallel the relationship between autonomic nervous system responsivity and valence of stimuli in humans. Given the relationship between human cardiac physiology and affective processing, these findings suggest that macaque cardiac physiology may be an index of affect in nonverbal animals.

A fundamental molecular feature of olfactory systems is that individual neurons express only one receptor from a large odorant receptor gene family. While numerous theories have been proposed, the functional significance and evolutionary advantage of generating a sophisticated one-receptor-per neuron expression pattern is not well understood. Using the genetically tractable Drosophila melanogaster as a model, we demonstrate that the breakdown of this highly restricted expression pattern of an odorant receptor in neurons leads to a deficit in the ability to exploit new food sources. We show that animals with ectopic co-expression of odorant receptors also have a competitive disadvantage in a complex environment with limiting food sources. At the level of the olfactory system, we find changes in both the behavioral and electrophysiological responses to odorants that are detected by endogenous receptors when an olfactory receptor is broadly misexpressed in chemosensory neurons. Taken together these results indicate that restrictive expression patterns and segregation of odorant receptors to individual neuron classes are important for sensitive odor-detection and appropriate olfactory behaviors.

Both avian and mammalian basal ganglia are involved in voluntary motor control. In birds, such movements include hopping, perching and flying. Two organizational features that distinguish the songbird basal ganglia are that striatal and pallidal neurons are intermingled, and that neurons dedicated to vocal-motor function are clustered together in a dense cell group known as area X that sits within the surrounding striato-pallidum. This specification allowed us to perform molecular profiling of two striato-pallidal subregions, comparing transcriptional patterns in tissue dedicated to vocal-motor function (area X) to those in tissue that contains similar cell types but supports non-vocal behaviors: the striato-pallidum ventral to area X (VSP), our focus here. Since any behavior is likely underpinned by the coordinated actions of many molecules, we constructed gene co-expression networks from microarray data to study large-scale transcriptional patterns in both subregions. Our goal was to investigate any relationship between VSP network structure and singing and identify gene co-expression groups, or modules, found in the VSP but not area X. We observed mild, but surprising, relationships between VSP modules and song spectral features, and found a group of four VSP modules that were highly specific to the region. These modules were unrelated to singing, but were composed of genes involved in many of the same biological processes as those we previously observed in area X-specific singing-related modules. The VSP-specific modules were also enriched for processes disrupted in Parkinson's and Huntington's Diseases. Our results suggest that the activation/inhibition of a single pathway is not sufficient to functionally specify area X versus the VSP and support the notion that molecular processes are not in and of themselves specialized for behavior. Instead, unique interactions between molecular pathways create functional specificity in particular brain regions during distinct behavioral states., Author Summary Understanding how gene transcription relates to behavior is challenging. Learned vocal-motor behavior is a complex trait that represents the output of multiple converging genes, pathways, and patterns of neural activity. Here, we applied a systems analytical approach to determine how thousands of genes change their expression levels simultaneously in a region of the vertebrate brain important for vocal-motor function, the basal ganglia, during a specific vocal-motor behavior, singing. We used the zebra finch species of songbird based on similarities between song learning/production and speech, and because they possess a set of brain subregions dedicated to singing. Microarrays were used to measure gene expression levels in one such song-dedicated region and in an adjacent motor area that is not thought to play a role in vocal function. This allowed us to address the question of whether distinct gene co-expression patterns could be found in each area. We found that each area contained unique patterns of transcriptional co-activity, but there were also unexpected overlaps. We conclude that the particular behaviors (singing versus non-vocal behaviors) supported by these subregions depend on the particular sets of interactions between molecular pathways that occur in each subregion.

Background: Pavlovian conditioning plays a critical role in both drug addiction and binge eating. Recent animal research suggests that certain individuals are highly sensitive to conditioned cues, whether they signal food or drugs. Are certain humans also more reactive to both food and drug cues? Methods: We examined cue-induced craving for both cigarettes and food, in the same individuals (n=15 adult smokers). Subjects viewed smoking-related or food-related images after abstaining from either smoking or eating. Results: Certain individuals reported strong cue-induced craving after both smoking and food cues. That is, subjects who reported strong cue-induced craving for cigarettes also rated stronger cue-induced food craving. Conclusions: In humans, like in nonhumans, there may be a ‘‘cue-reactive’’ phenotype, consisting of individuals who are highly sensitive to conditioned stimuli. This finding extends recent reports from nonhuman studies. Further understanding this subgroup of smokers may allow clinicians to individually tailor therapies for smoking cessation.