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Computational neuroscience

The last several years have seen a tremendous growth in the use of computer modeling within neurobiology (Bower, 1992). As experimental data continues to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. This combination of modeling and experimental work has led to the creation of the new discipline of computational neuroscience (Eeckman & Bower, 1993). As computer power continues to grow, it is inevitable that the size and detail of neurobiological models will also continue to expand. This poses new problems to be solved for those working in computational neuroscience, and new challenges for informatics research.

As the complexity of constructed models, and we would claim, their predictive power continue to increase, it becomes more clear that efficient and informed flow of information between modelers and experimentalists will be increasingly important. Traditionally, modelers have obtained the information they need to construct models through the published literature, while those experimentalists interested in modeling have also had to understand models through published accounts. Even for those still relatively small numbers of neurobiologists who combine modeling and experimental work, a great deal of information is still obtained through the printed literature. However, it is already clear that this awkward form of information exchange will not be able to support the continuing growth in model sophistication (Bower & Koch, 1992) or the potential for modeling to inform experimental research (Hasselmo & Bower, 1993). Instead, we must explore new means of transferring information about the nervous system and nervous system related models.

User survey of informatics needs

During the first year of our participation in the Human Brain Project, we carried out a survey of the informatics needs of practicing neurobiologists, with an emphasis on the needs of those who carried out modeling studies in addition to experimental research. This was achieved with laboratory meetings with neurobiologists, and their responses to a hypertext electronic questionaire administered over the World Wide Web. From these questionaires we were able to isolate a number of tasks that the neurobiologists agreed were an accurate characterization of their research process. These tasks include:

  1. Experimentation - Electrical activity from neural tissue is recorded and the amplified signals are digitized and stored on a hard disk. Each recording session is stored in a separate flat file.

  2. Simulation - A simulation is constructed, based on experimental data which is either obtained directly or taken from the literature. In the case of single cell modeling, this consists of building a neuron out of a number of discrete compartments and tuning the properties of the compartments, based on the experimental data. This often involves the addition of of a number of ionic channels which are known to exist in the neuron, and the adjustment of the parameters which characterize their behavior. The simulation results are then compared with other experimental findings in order to validate the model.

  3. Data Analysis - Experimental or simulation data is translated to formats that can be imported into statistical analysis packages. Typically, these translation programs must be written by the researcher. Additional visualizaton tools may also be applied in order to gain greater insight into the data.

  4. Journal Research - This is similar to research that goes on in most scientific disciplines. Journals and books are searched for relevant material. References are tracked to obtain further relevant information. Photocopies may be made for particularly relevant material which may include graphical as well as textual data. For example, it may include plots of experimental data which will be used to characterize ionic channels which will be incorporated in the model.

  5. Personal Note Taking and Data Management - Personal notes and notebooks are kept to log experimental techniques and map the search space of possible research paths. Additional notes consist of diagrams, graphs, equations, simulation parameters for experimental and simulation work. Data management involves the storage of data from experiments, simulations and results of analyses, in flat files for later use.

  6. Publishing Results - Results are published through standard peer-reviewed mechanisms such as journals and conference proceedings. Various image conversions are performed to transfer graphs from analysis packages and drawing programs to word processors. Bibliography lists have to be compiled.

The following is a compilation of what the neurobiologists felt would be useful to their research:

  1. It would be useful to integrate the simulation system, and data from experiments, with a data analysis and visualization system. That is, the neurobiologists want a set of tools to convert data from their experiments and simulations to formats that can be read by their analysis and visualization tools.

  2. It would be useful to maintain an electronic library of neuroscience literature from which some of the parameters used in modeling work can be systematically collected to allow easier access and comparison.

  3. It would be useful to have a means of maintaining a large collection of modeling data. Multiple simulation models for perhaps the same neuron should be stored to allow comparison. Simulation parameters should be retrievable for other simulation work. Also, it is important that models stored in this collection have references to the literature and information on the experimental techniques from which data were derived. There should be an automatic mechanism for linking modeling data with experimental data and information found in electronic copies of neuroscience literature.

  4. It would be useful to maintain a personal database of notes, diagrams, equations, experimental and modeling data.

next up previous
Next: CHALLENGES PRESENTED BY Up: The GENESIS Simulator-based Neuronal Previous: INTRODUCTION

Dave Beeman
Wed Oct 11 15:28:38 PDT 1995