Generation of Metachronal Motor Patterns in Segmented Nervous Systems

University Of California-Davis, Davis CA

Investigators

Abstract

Locomotion in vertebrates and arthropods is a complex behavior, and the neural mechanisms that coordinate their limbs during locomotion are not well understood. The abdominal swimmeret system of the crayfish provides a relatively simple and accessible system to study the intersegmental coordination of movement. Within each segment of the tail, the swimmeret paddles show an alternating power stroke and a return stroke, as in most locomotor appendages, and the strokes of adjacent segments show specific timing relationships. There are small circuits of neurons (nerve cells) within each segment that have known identifiable single cells driving particular motor movements, and three types of interneurons that are involved in coordinating the movements. It is particularly useful that the crayfish ventral nerve cord and swimmeret system can be isolated in vitro and still show the intersegmentally coordinated swimming pattern. This project uses a combined physiological and computational modeling approach to define the dynamic circuitry for the pattern. A cellular model of the nerve cell physiological properties and network connections is used to predict the phase patterns of bursts of nerve impulse activity of the relevant neurons in the system. These predictions are compared with the activity patterns and connectivity in individual neurons recorded physiologically in the living circuit, including the small electrical potentials at synaptic connections between pairs of single neurons as well as the impulse traffic between segments. The relative importance of local and intersegmental pathways are assessed by stimulating individual coordinating interneurons when the intersegmental pathways are intact and when they are blocked. Intracellular dye fills anatomically confirm physiological connecting pathways. Refinements of the model by the experimental findings will yield a thorough description of how the dynamics of the interneurons contribute to stable intersegmental coordination, and how the properties of the synapses contribute to normal locomotion. This work will have an impact beyond crustacean locomotion research, by leading to new insights about neural mechanisms that underlie the much more complex vertebrate circuits for locomotion. The integration of computational modeling with physiology will also have an impact on needed multi-disciplinary training of students and postdoctoral researchers in neuroscience and physiology in general.