Neural Models of Plasticity: Molecules to Networks

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DESCRIPTION (provided by applicant): The main function of the nervous system is to process information in ways that lead to adaptive behavior. Two different approaches, one theoretical and the other empirical, are being used to explore the role of neuronal plasticity in development, learning, memory, information processing, and other complex brain functions. The theoretical approach simulates and synthesizing brain function with mathematical models based on known and hypothesized principles of neural function. The empirical approach delineates the complex biochemical and biophysical properties of neurons, the rules that determine their connectivity, and the mechanisms through which their properties and connections are modified during development and learning. Although these two approaches have traditionally been used independently, there is a growing realization among neurobiologists, psychologists, and adaptive systems theorists that progress in understanding the brain is dependent on a combination of both approaches. In addition, in many cases, the knowledge of systems has matured to the point where there is not only a sufficient body of information to warrant a computational approach, but further progress in the understanding of the system requires it. The overall goal of the Program Project is to use computational approaches to examine neuronal plasticity at multiple levels of organization, ranging from molecular dynamics within subcellular neuronal compartments, to genetic networks within neurons, to neural network mechanisms. The individual Projects are linked by the common goal of investigating plasticity in neurons in the hippocampus and related structures and determining its contributions to higher levels of processing. The individual Projects will examine: 1) the dynamical properties of gene networks underlying plasticity; 2) the quantitative behavior of the postsynaptic Ca2+/calmodulin signaling pathway that plays an essential role in neuronal plasticity; 3) dynamics of synaptic plasticity at the molecular level and its importance as a substrate for plasticity in the hippocampus; and 4) the neural network mechanisms by which the hippocampus constructs high-order cognitive representations from multimodal inputs. In addition, the individual Projects will be supported by a Computational Core Facility that will serve as a resource for developing computational models and for the exchange of information among the projects.