EIA-0130883-Theodore W. Berger-University of Southern California-Title: Neurobiological Nonlinear Dynamics for Biomimetic Signal Processing-Title-The fundamental goal of the proposed research is to derived a new generation of temporal and spatio-temporal pattern recognition systems based on the nonlinear dynamics, network architecture, and synaptic plasticity properties of the hippocampus, a cortical brain system responsible for the formation of new pattern recognition memories. From a neurobiological perspective, the proposed experimental/modeling work promises to generate (1) first-characterizations of high-order nonlinearities of cortical brain tissue, i.e., predictive models of the input/output transformations in spatio-temporal activity performed by individual hippocampal neurons, and to (2) investigate the increasingly likely possibility of dynamic neural "learning rules", i.e., requisite conditions for the induction of synaptic plasticity that depend on the past history of activity. In addition, the proposed research will investigate (3) the role of known hippocampal network topology in neurobiological signal processing and hierarchical feature extraction. From a theoretical/computational perspective, the proposed work is designed to (4) develop novel methodologies essential for characterizing nonlinearities of neurobiological systems, as well as to (5) further expand a newly developed paradigm for biologically realistic neural system modeling (the "dynamic synapse neural network architecture") that has already demonstrated a heretofore unmatched capability for identifying optimal feature sets for temporally and spatio-temporally coded information.
