MRI: Acquisition of Instrumentation for Coupled Experimental-Computational Neuroscience and Biology Research

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Proposal #: CNS 08-21622PI(s): Fortes, Jose A. McIntyre, Lauren M.; Moroz, Leonid L.; Principe, Jose C.; Sanchez, Justin C.Institution: University of Florida Gainesville, FL 32611-2002Title: MRI/Acq.: Instrumentation for Coupled Experimental-Computational Neuroscience and Biology ResearchProject Proposed:This project, acquiring a virtualized multicomputer instrument with shared-memory subsystems and storage capacity, intends to use this instrument as a shared instrument whose resources can be virtualized, reserved, and configured on demand for different research activities related to neuroscience, computational biology, and cyberinfrastructure. Its configurations can also be dedicated and tightly coupled to in vivo experiments using network connections to in situ instrumentation used for experiments. It can simultaneously support multiple research activities because of its unique capability to support real-time computer-in-the-loop experiments, its ability to run many concurrent computation threads, its shared memory and storage subsystems, and its use of virtualization technology to manage the coexistence of multiple computing environments. To develop and validate autonomic computing nodes and techniques for multiuser virtualized computational systems, the instrument provides traces of performance and other needed monitored data. Research activities in brain-machine interfaces, neurogenesis, genomics, bioinformatics, signal processing, cyberinfrastructure, autonomic computing and other areas include:- Brain-machine interfaces where cortex models for motor control are dynamically learned and applied in real-time,- Experimental drug discovery through real-time analysis of large amounts of genetic data and many thousands of compounds,- Analysis of Terabytes of genetic data captured in real-time as a neuron grows, learns, and remembers, and- Online learning algorithms using dynamic filter topologies with online computation requirements that increase over time.The activities have transformative goals, including the introduction of real-time, and address the high performance computation into closed-loop experiments and/or systems whose behavior is driven by complex processing of sensed data. Another goal involves providing off-line computing capabilities to match the unprecedented rate and volume of genetic data produced by massively parallel DNA sequencing technology.