RCN: The Pyloric Model Group: Functional Analysis of a Complex, Distributed Biological Neural Network

Ohio University, Athens OH

Investigators

Abstract

Complex, distributed systems are those in which the components are highly interconnected and individually non-linear in operation. We have little understanding of how such systems operate. Nervous systems are highly complex and non-linear, and this lack of understanding is an impediment to understanding nervous system function, and to designing computational devices that can mimic nervous system capabilities. Experimental and computational studies of advantageous animal models have led to some useful advances. This proposal describes such an approach using a small network of nerve cells (neurons) called the pyloric neural network, part of a larger complex called the stomatogastric ganglion, which generates rhythmic activity in the gut of crustaceans. This network generates a suite of behaviors and is small, extremely well understood, and very amenable to experiment and computational modeling. These investigations have been done in many different laboratories, with a far broader scope than any one or two labs could approach, and this Research Coordination Network is established to coordinate their efforts. The Pyloric Model Group is set up to construct a computational model of the pyloric network, to supervise a postdoctoral fellow who will help fill critical gaps and construct the model, to sponsor annual meetings to share the knowledge of the pool of labs that conduct stomatogastric ganglion research, and to share the results of the collaboration with the broader scientific community by Web and CD distribution. This project will have an impact on the fundamental understanding of the neurobiological basis of behavior, will provide insights into the function of complex distributed systems in general, will promote cross-disciplinary collaborations including small colleges with major universities, and will broaden the experience of students in a cross-disciplinary environment.