PROJECT SUMMARY All motor commands flow through motoneurons in the spinal cord and brainstem. As
for inputs to neural circuits throughout the CNS, these commands comprise three main components: two types
of ionotropic input (excitation and inhibition) and a set of G-protein coupled inputs (neuromodulation). Lack of
understanding of how these components produce output constitutes a fundamental uncertainty at the
foundation of the neural control of movement. Fortunately, motor output in humans can be studied at the level
of single neurons. Motoneuron action potentials are 1-to-1 with those of their muscle fibers, forming motor
units whose action potentials can be recorded relatively easily in muscles. The potential for using these motor
unit firing patterns for understanding motor commands has long been appreciated. Our goal is to maximize
this potential by developing supercomputer-based techniques for reverse engineering motor unit firing patterns
to identify the amplitudes and patterns of the excitatory, inhibitory and neuromodulatory inputs underlying
motor commands in humans. Recent advances that allow simultaneous recording of many motor units have
allowed us to identify distinctive nonlinear behaviors in motor unit firing patterns. Our development of realistic
models of motoneurons show that these nonlinearities arise from complex interactions between input
components. We plan to use these models as the core of a reverse engineering (RE) approach that estimates
these three components from nonlinear human motor unit firing patterns. Our premise is that implementation
of our models on supercomputers at Argonne National Laboratories will allow systematic exploration of the
firing patterns generated by many thousands of input combinations. Those input organizations that accurately
recreate a measured set of firing patterns will then be considered to be part of the “solution space” for that
particular motor output. The key problem for this analysis is redundancy. If the same motor output can be
produced by many input combinations, then reverse engineering will reveal huge solution spaces that provide
little insight into motor commands. Overall motor outputs like force and EMG suffer from this problem. Our
concept, however, is that measuring motor output at the single neuron level, via motor unit recordings, allows
for effective reverse engineering. We have 3 aims: 1) to develop and evaluate supercomputer-based reverse
engineering techniques for analysis of motor unit firing patterns. 2) to deploy RE to investigate the
mechanisms of muscle-specific differences in populations of motor unit firing patterns. And 3) to deploy RE to
investigate whether inhibitory-neuromodulation interactions that are specific for each muscle are relatively
fixed, or instead are continuously adapted for different motor tasks. The development of supercomputer-based
analysis techniques provides an ideal complement to emergence of techniques to measure firing patterns of
large populations of motor units. Our novel reverse engineering method have the potential to transform our
understanding of the synaptic organization of motor commands in humans.