The goal of this project is to discover principles underlying the coordination of limbs and opposing muscles during walking by combining biological experiments with new dynamical models of the spinal neural networks that generate hindlimb movements in the mouse. Since walking is impaired in the large group of spinal cord injured patients, it is of importance to know the normal function of locomotor networks. The isolated mouse spinal cord gives good access for electrophysiological investigation of the neural circuits that control walking. New genetic techniques for specifically labeling or ablating specific classes of spinal neurons are likely to produce novel information about the physiology and anatomy of these networks. Because the networks are complex, computer models are needed in order to understand how the network works. Research on improved methods for fitting models of rhythmic processes to dynamical data will lead to new algorithms for parametrizing these models. The computer models to be developed in this project will be coupled cell systems of differential equations for membrane currents, whose structure incorporates what is known about the spinal cord. This project establishes a new collaboration to develop realistic models that build upon Kiehn's long experience and expertise with this system. Experiments will be conducted both at Cornell University (Harris-Warrick) and at the Karolinska Institute, Sweden (Kiehn) to measure the physiological properties of neurons and their synapses. In addition, connectivity of the network will be studied in the laboratory. ? ? The specific aims of the project are to: ? ? 1. generate current-based Hodgkin-Huxley-type models of the interneurons that coordinate the oscillator networks in the spinal cord; ? ? 2. characterize intrasegmental coordination of spinal pattern generation of hind leg movements; ? ? 3. analyze left-right coordination in mutant """"""""hopping"""""""" mice, and develop algorithms for the parameterization and analysis of network models of central pattern generation. ? ?
