All the NIH funded neuroscientist in this proposal generate, model, and analyze data from complex neural systems. They work across disclipinary lines and each has begun to combine experiment and computation. There are two underlying reasons for this. First, the understanding of the appropriate solutions to their research questions has recently had conceptual breakthroughs. The second reason is that it is now technically possible to perform the extensive and more difficult computations necessary to test these solutions. Problems in computational neuroscience generally involve many contingently interactive parts. For example in cortical simulations, a model of the neuron can involve thousands of compartments. The simulation of a region of the brain can involve multiple contingent interactions between millions of neurons. This combinatorial explosion leads to tremendous computational demands; demands that were not met by computational machines of the last threc decades. The research studies of this proposal focus on the use of computations to critically test neuroscience questions. This grant primarily requests funds for the further development of the high performance, Neuroscience specific parallel computational components. Funds are requested for purchasing additional computational nodes on the IBM SP2 parallel computer, additional disk storage, and tape archiving. Funds are also requested to provide paradigm-specific high speed ATM network access to the laboratory bench . The high-performance parallel computing will permit the faculty, post-doctoral trainees, and graduate students to compute reasonably sized neuroscience problems, and do this in conjunction with experimental procedures. Importantly the significant local computational power will permit on-line model and data-steering via the use of a visualization engine in parallel with the computational engine. The high Speed network will permit the integration of the parallel computation and visualization with the experimental data collection.