The brain and behavioral sciences provide difficult challenges to computer scientists, in areas of information retrieval and database technology, simulation systems, visualization tools, and high performance computing. These challenges stem from two fundamental characteristics of the data involved in brain and behavioral sciences. First, the quantity of collectable (and collected) data is undergoing exponential increase. This increase is due to rapidly developing technologies for data acquisition including imaging modalities such as functional magnetic resonance imaging (fMRI) and electrical sensing techniques such as multi-electrode mult-unit recording. Second, the data collected in these sciences and the models constructed from them are extremely heterogenous in nature. This is largely due to the underlying complexity of the systems being examined and the huge ranges of distance (e.g., molecular to organ) and time (microseconds to years) that are of interest. There are few other areas of science that exhibit both of these two characteristics to such an extreme.
These characteristics impose difficult requirements for many areas of informatics research. Information retrieval and database technologies are just beginning to seriously address extremely heterogenous data. In part, the development of object oriented database systems (Grossman & Qin, 1993) has been motivated by the need for databases that can manage heterogeneous data. There has been a large effort in creating simulation systems (including the GENESIS simulator) that provide a modeling environment capable of handling the complex models developed in the brain and behavioral sciences. Visualization techniques are particularly difficult for these sciences. It is often not appropriate to view the data as a multi-dimensional block, since we already know that the systems generating it are highly structured. Scientists are interested in viewing the data at spatial scales spanning many orders of magnitude, as described above, and often in viewing time series at multiple temporal scales.
In common with other sciences that generate large quantities of data that must be analyzed and modeled, behavioral and brain sciences force the development of terabyte scale file systems and computer hardware and software that to match. Multi-resident distributed file systems and RAID arrays are two examples of technogies developed to meet the need for large scale storage. A particularly interesting current development is large scale parallel machines and software. Although parallel hardware is being developed for technical reasons, the range of software available to program it will be significantly extended by the demands of the behavioral and brain sciences. The heterogeneity of the data, the range of spatial and temporal scales that must be accomodated, and the volume of data to be dealt with, mean that sophisticated parallel programming tools will need to be developed (Feldman, 1994).