The goal of this award is to develop an information handling system that will make routine the storage, organization, retrieval, analysis and sharing of experimental and simulated neuronal data. Much current knowledge about brain function is based on analysis of firing patterns of individual neurons. Neural modeling generates massive simulated data sets that need to be processed, analyzed and compared with experimental data. The ultimate aim is to develop a set of tools, techniques and standards that can be disseminated to help meet the needs of a large community of neuroscientists who work with neuronal data. An information system for neuronal time-series data involves several interrelated components. The first is a database component for organized storage of large data sets, and efficient search and retrieval of data meeting specific criteria of interest. A second component is the establishment of conventions for data representation and transfer so that time-series records can be processed independently of the format in which they are stored. The third component is a suite of tools for data analysis and visualization that provides a flexible and intuitive means of processing the data. To achieve full operational status several steps are required: a) activation to full on-line status of the currently participating pilot laboratories; b) enhancement of the database search and query functionality; c) specification and implementation of the time-series data protocol (TSDP); d) development of TSDP-compatible analysis and visualization tools. Finally, these steps must be carried out within an ongoing cycle of performance testing, evaluation and improvement.
