Research Project
10226040
Project Summary: Project 1 ? Modeling Forelimb Motor Circuit Organization and Function This Research Project addresses the dynamics of motor circuits in the cervical spinal cord. Aims in this project will analyze how cervical spinal networks coordinate forelimb movement during rhythmic and non-rhythmic activity. The model will include neuronal elements (excitatory and inhibitory pre-motor neurons and antagonist pools of motor neurons), antagonist pairs of muscles, and mechanical components of the limb. Theoretical and computational techniques will be used to describe how these elements interact, including possible responses to descending brain commands, to generate forelimb activities such as isometric and isotonic movements. The first goal will be to compute how cervical spinal motor neurons fire in response to inputs (based on slice electrophysiology experiments, Projects 2 and 3), and how their firing is converted to muscle force (based on EMG recordings, Project 4). A second goal will be to analyze circuit configurations that achieve maximum control in order and predict new functional cell types that may be present in the spinal cord. Network models composed of motor and pre-motor neurons will then be constructed, defining the patterns of network firing and bursting of antagonistic motor pools, corresponding to elbow and wrist movements and static states. The model will be constrained by connectivity data (Project 2), and informed by theoretical analyses of optimal circuit configurations. Finally, a mechanical model of the limb will be connected to the neuronal model to generate a coherent picture of the transformation from descending input to the cervical spinal cord to limb movements. The dynamical responses of the model will be compared with the behavioral and in vivo electrophysiological data collected in Project 4. Overall, the theoretical analyses performed here, together with experimental investigations from Projects 2-4, will help inform general principles concerning how multiple neuronal types coordinate their response properties to achieve robust control of multiple dynamical variables.
