Knowledge management of the neuroscientific literature: the data model and underlying strategy of the NeuroScholar system.
This paper describes the underlying strategy and system's design of a knowledge management system for the neuroscientific literature called 'NeuroScholar'. The problem that the system is designed to address is to delineate fully the neural circuitry involved in a specific behaviour. The use of this system provides experimental neuroscientists with a new method of building computational models ('knowledge models') of the contents of the published literature. These models may provide input for analysis (conceptual or computational), or be used as constraint sets for conventional neural modelling work. The underlying problems inherent in this approach, the general framework for the proposed solution, the practical issues concerning usage of the system and a detailed, technical account of the system are described. The author uses a widely used software specification language (the Universal Modelling Language) to describe the design of the system and present examples from published work concerned with classical eyeblink conditioning in the rabbit.
Burns
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Department of Neurobiology, University of Southern California, Los Angeles, CA 90089-2520, USA. gully@usc.edu
D000818:Animals; D016208:Databases, Factual; D008959:Models, Neurological; D009488:Neurosciences / Q000295:instrumentation* / Q000379:methods*