A crucial problem in neuroscience is to understand how the brain coordinates muscle activity to produce behavior. For decades, researchers have approached this problem by recording spiking activity in motor areas and attempting to understand how cortical spike trains control motor output, which is characterized either by the behavior itself (e.g. hand trajectories) or by standard EMG methods (which provide a bulk, multiunit measure of muscle activity levels). Recent advances in both neural recording hardware now allow spike trains from a large number of neurons to be recorded simultaneously. Furthermore, novel computational methods for analyzing large datasets of concurrently-recorded neural activity have led to dramatic new insights into how cortical networks encode behavior. However, there are currently no methods for recording large spiking ensembles in muscle cells during behavior, hindering our understanding of how the nervous system ultimately coordinates movement. This unmet need hinders our understanding of brain function, both from the perspective of comprehending the normal function of the nervous system and in designing clinical applications that might use EMG activity to enable patients to control prostheses. This proposal builds on the recent invention (by PI Sober's group) of a novel class of flexible electrode array that sits on the muscle surface and provides single-unit recordings of individual motor units, the collection of muscle fibers innervated by a single motor neuron. We will build on these preliminary results to develop and validate technologies for recording the entire ensemble of muscles in both the songbird vocal organ and the mouse forelimb. To do so we will advance the current design by scaling it up the channel count by orders of magnitude, developing species- specific array morphologies that can record units from all relevant muscles simultaneously, embedding active circuitry within the flexible array to allow data multiplexing, and creating and distributing software tools for processing and analyzing the resulting datasets.
This research will develop new technologies for recording electrical activity in the muscles that control skilled behavior. Such techniques, which are not currently available elsewhere, may be used to improve clinical measurements of nervous system function and to design rehabilitative therapies.