CRI: Reliable and Adaptive Behavior of Neurons: Experiment and Theory

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IBN: 9634405 PI: Abarbanel The last ten years has seen a huge increase in the number of computational studies aimed at understanding the brain. This is largely the result of an enormous expansion of computer power as well as promising new programs which are useful in studying neural-like phenomena such as pattern recognition and memory formation. During this period there has also been a parallel increase in our knowledge of small nervous systems at the cellular and circuit level. Because the brain is so complex in terms of the number of cells and connections between them, smaller invertebrate nervous systems, which operate in exactly the same manner as larger ones, can serve as models for understanding fundamental properties of information processing. There are now many small circuits which have been completely described in terms of synaptic connectivity. While such circuits can be modeled phenomenologically, there is a lack of deep understanding of the fundamental principles by which they operate. Our approach will be to look for such general principles in the thirty celled stomatogastric nervous system of the lobster. We will use new mathematical techniques which have been so successful in physics, to provide a dynamical, nonlinear analysis of the rhythmic motor output patterns generated by the ganglion. These methods have not yet been applied to studies of the nervous system at the level of single identified neurons and small neural circuits. This project is both special and timely because it has the ability to move back and forth between theory and experiment at a level of resolution not possible in other systems. Using this approach, we hope to provide a new and generalizable description of how neural networks produce and process information so as to achieve reliable, robust and adaptive behavior for the animal. We believe a cross-disciplinary synergy such as we describe here, holds the key for future developments in robotics and artificial intelligence as well as providin g basic information about neural processing.