Bence P. Olveczky - US grants

DESCRIPTION (provided by applicant): Learned motor sequences underlie most of human communication, yet remarkably little is known about how the nervous system learns to control the complex muscle actions involved. Our long term goal is to describe the neural circuit mechanisms underlying the acquisition of learned motor behaviors. The zebra finch, a songbird, provides a unique system in which to pursue this goal, as it acquires its song in much the same way that we learn many of our motor skills, including speech. Aims: Our proposal aims to describe how the motor program for song develops by recording from neurons in a motor cortex analogue structure (nucleus RA) in the freely behaving, juvenile zebra finch throughout song learning (Aim 1). Widely thought to be the site of vocal learning, RA receives convergent input from a higher order motor area, HVC, and from a basal ganglia circuit. The proposal will examine the respective roles of these two inputs in shaping the motor command in RA during learning (Aim 2). We examine how auditory feedback-based performance evaluation, a crucial ingredient for both song learning and song maintenance, influences the development of the motor program (Aim 3). Lastly, we assess the extent to which the learning induced changes in the RA motor program are driven by changes in HVC, its premotor input (Aim 4). Methods: The proposal will examine these issues with a combination of powerful methods: custom- made motorized microdrives will allow the recording of single neurons in RA in the singing, juvenile bird, and a chronically implanted reverse microdialysis device will make possible the fast and reversible inactivation of the basal ganglia circuit. Finally, we will use mathematical models that incorporate our observations into a biophysically plausible model of how the song circuit learns and functions. PUBLIC HEALTH RELEVANCE Our experiments aim to describe how the motor program underlying a complex motor behavior evolves, and the logic by which the motor circuits underlying it are organized with respect to learning. The homologies and analogies between the neural circuits generating vocalizations in songbirds and humans are many, thus our findings will also speak to the question of how the motor program underlying speech and other learned motor behaviors may be acquired. Understanding the neural correlates of complex motor learning will allow us to pinpoint how the process may fail, thus addressing the possible causes of various motor disorders and disabilities.

Neural circuits underlying the acquisition and control of motor skills Much of our behavioral repertoire consists of learned motor skills, yet little is known about the neural mechanisms that underlie their acquisition and execution. We have recently discovered that motor cortex is required for learning but not for executing certain motor skills, suggesting an autonomous subcortical motor network capable of generating task-specific learned motor sequences. Importantly, motor cortex seems to be involved in ?tutoring? this subcortical network during learning. Here we will first explore the role of the basal ganglia, a collection of motor-related midbrain nuclei of great clinical significance, in the storage and execution of complex task-specific motor sequences. Specifically, we will test whether the part of the basal ganglia that receives input from motor cortex, the dorsolateral striatum (DLS), is essentially involved in producing the skills we train. We will do this by way of lesioning DLS and other parts of the basal ganglia in animals that have learned to master the task we train (Aim 1). We will further analyze how the striatum encodes the learned motor sequences, specifically testing the hypothesis that it encodes the detailed structure and kinematics of learned motor sequences (Aim 2). Lastly, we will test the idea that motor cortex is ?tutoring? the subcortical motor circuits during learning through its projections to the basal ganglia (Aim 3). We will explore these questions using a fully automated rodent training system we developed, in combination with a set-up for recording neural activity and behavior continuously over weeks and months in freely behaving rodents. Addressing the aims of our proposal will clarify the logic of how the mammalian motor system acquires and controls task-specific motor sequences, and delineate the roles of the BG and the corticostriatal pathway in these important processes, thus addressing fundamental questions in neuroscience with far-reaching implications for clinical practice and neurorehabilitation.

This proposal aims to experimentally and computationally study how the brain learns and generates complex sequences of actions. Since behaviors emerge from the dynamic interplay of many brain areas, computational models that permit the study of distributed circuits are important. Such models can unify results across multiple experiments and brain areas to provide insights into neural circuit computations and, importantly, to generate experimental predictions. To provide experimental constraints for such models, a motor learning paradigm is needed in which subjects can be reliably trained, their behavior quantified, and neural activity in relevant circuits measured and manipulated. The rodent motor sequence task developed and studied in the Olveczky lab conforms to these criteria, and hence can serve to constrain, refine, and arbitrate between different computational models, and serve as an experimental test-bed for their predictions. The computational effort, led by the Escola lab, will start with an exploration of circuit models consistent with available data. Further analysis of these models will generate competing predictions and ideas for experiments that arbitrate between them. The goal of this collaboration is to arrive at a circuit-level description of how complex motor sequences are learned and produced in the form of a biologically plausible computational model that can be refined and updated as new experimental results arrive. Recordings from neurons in the striatum (a major motor system structure) reveal the existence of sequence cells-- neurons that are sparsely active and precisely time-locked to the motor output. Additionally, the Olveczky lab showed that motor cortex is essential for learning the task, but can be lesioned without impairing task execution. These results raise important questions: 1) Is sequence cell activity in the striatum driving the learned motor sequences, and if so, is this dynamics generated independently of motor cortex? 2) How do other motor-related circuits contribute to striatal dynamics and ultimately behavior? 3) How do the circuits controlling complex motor sequences learn, i.e. where are the sites of plasticity within these circuits and what learning rules govern their adaptive reorganization? A computational modeling framework, constrained by experimental results, which rigorously and quantitatively addresses these questions, will advance our understanding of how the motor system produces learned motor sequences. This understanding will provide a new framework within which to consider the pathogenesis of movement <11r.h Prkin>< nrl H11ntinntnn'>< rli>

PROJECT SUMMARY The aim of this proposal is to deliver an innovative and easy-to-use experimental platform for measuring and quantifying naturalistic behaviors of mammalian animal models used for biomedical research, including rodents and monkeys, across a range of spatial and temporal scales. This will require developing a method for tracking movements freely behaving animals with far higher spatiotemporal resolution and more kinematic detail than currently possible. To overcome the limitations of current technologies, a new solution is proposed that synergistically combines two methods - marker based motion capture and a video- based machine learning approach. First, using marker-based motion capture, the gold standard for 3D tracking in humans, the position of experimental subjects' head, trunk, and limbs will be tracked in 3D with submillimeter precision. An innovative marker design, placement strategy, and post-processing pipeline will ensure an unprecedentedly detailed description of rodent behavior over a large range of timescales. To make the system more efficient, robust, affordable and better suited for high-throughput longitudinal studies, the unprecedentedly rich and large 3D datasets generated by the motion capture experiments will be leveraged to train a deep neural network to predict pose and appendage positions from a set of 1-6 normal video cameras. To best capitalize on the large training datasets, the latest advances in convolutional neural networks for image analysis will be incorporated. Together, these advances will promote generalization of the high-resolution 3D tracking system to a variety of animals and environments, thus establishing a cheap, flexible, and easy-to use kinematic tracking method that can easily be scaled up and adopted by other labs. The large ground-truth datasets will allow the system to be benchmarked and compared against state-of-the art technologies in quantitative and rigorous ways. Preliminary studies have been very positive and suggest large improvements over current methods both when it comes to the range of behaviors that can be tracked and the precision with which they can be measured. Importantly, all new technology will be readily shared with the scientific community, thereby leveraging from this single grant the potential for numerous investigators to dramatically improve the efficiency of their research programs requiring rigorous quantitative descriptions of animal behavior.