An integrated environment for modeling, experimental databases and data mining in neuroscience

In this thesis I propose an integrated framework for a modeling and simulation system, experimental databases, and a data mining system. A middle-ware layer is developed on top of the components to facilitate the connections among them. This is a multi-disciplinary study falling in the intersection of neural modeling, experimental time series analysis, database systems and computer science. Traditionally, modeling, experimental studies and database systems are separated with their own approaches. An integrated environment with emerging database techniques will provide more powerful tools to both modelers and experimentalists, and help them to get insights from each other's work. To achieve the objective, we develop a system architecture with five components: graphical user interface, a modeling and simulation system, experimental databases, a data mining system, and a middle-ware layer, with emphasis on the last four components in this thesis. Each of the components is an independent system with its specific characteristics, which can communicate with the other components. We provide methods to enhance the collaboration among them.; Individual components in the environment have been developed first, including a multi-level modeling system EONS, a prototypical data mining system NeuroMiner, an index structure (Sh-index) for experimental time series and a data warehouse architecture for the experimental databases to support data mining. Moreover, emphasis has been put on developing the Model/Data Integration System (MDIS) for linking modeling and experimental studies. This architecture serves as a middle-ware layer for modeling and experimental databases, and is then extended to including data mining components. A common representation for message passing through XML and an application-dependent interaction protocol for facilitating the communication among the components are provided.; The developed system architecture is loosely coupled and leaves room for extending individual components and adding new components into it. Each component can be modified and/or extended on its own without affecting the performance of other components. This multi-tiered architecture separates interface, logic and data for maximum deployment flexibility. It also makes it easy for us to adopt a component-based approach to implement the system.