A grand challenge in neuroscience is to understand the biological basis of information processing at the cellular and network levels. One major technological barrier to continuing progress is the lack of adequate informatics and analysis tools to enable exploration of the structural organization of complex neural circuits, the discovery and understanding of emergent properties of neural ensembles, the formulation of quantitative hypotheses about neural computation, and the testing of those hypotheses experimentally. We have developed a prototype database system that provides many of the needed capabilities, and have used this software system to study mechanisms underlying neural encoding. The general goals of the work proposed here are to extend the capabilities and general utility of this prototype system, to interface the database with several additional tools for the analysis of structural and time-series data, and to enable more effective data sharing between remote collaborators. The core informatics and neuroscience research will be carried out by a group of researchers at Montana State University. Collaborations have also been established with researchers at several other research institutions, to insure interoperability of our system with other data collection and analysis tools and to facilitate the testing and refinement of our system. The investigator and co-investigators are all funded through NIH to study dynamic aspects of sensory processing at the cellular and network levels in a variety of preparations, including the cat and monkey visual systems. The researchers all share the following general neuroscience research aims: a) to understand the relationships between spatio-temporal activity patterns in neural ensembles and the information they convey, b) to understand how the spatio-temporal patterns at one processing stage are decoded at the next processing stage, c) to understand how computations are carried out on that decoded information, and d) to understand the mechanisms through which the observed dynamical patterns emerge from the morphology, synaptic connectivity, and intrinsic biophysical characteristics of the neurons in the ensembles. The software tools developed here will be applied to these ongoing studies, enabling a substantial increase in the breadth, depth, rigor, and rate of progress of those research projects. That neuroscience research will, in turn, provide a rigorous basis for the refinement, generalization and extension of the informatics tools.
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