Designing a neural network simulator—the MENS modelling environment for network systems: I
Max-Planck-Institut fur Himforschung Deutschordenstr, 46, 6000 Frankfurt 71, FRG
During recent years, the field of neural network research has increasingly attracted the interest of workers from a large number of different disciplines. Current research topics include aspects as different as detailed simulations in brain physiology, predictions of protein structure in biochemistry, database organization in computer science, or various technical applications. The common scheme behind these different approaches is the use of distributed networks of simple computational elements that communicate with each other by means of weighted links. Computer simulations of neural networks require an appropriate software environment. Due to the computational similarities of many classes of such networks, simulation software can be structured into modular components that, to a large degree, are independent of specific applications. The aim of this and the following paper is to discuss some of the design considerations concerning software for neural network simulations. The aspects presented are interesting for both the development of new simulation software and the efficient use and modification of existing programs. Therefore, the general user as well as the software designer may hopefully benefit from this material. This paper briefly introduces some of the basic principles of neural networks. After a short discussion of different approaches to software design, two simple example applications are presented in order to demonstrate a conceptual framework common to many network simulations. The transfer of these considerations to the design of simulation software is then shown by example of the MENS network simulator developed in the Max-Planck-Institute for Brain Research. The paper gives a general introduction to the layout of data structures and different software components. Using the two introductory examples some aspects of network analysis are demonstrated. The following paper then considers further details of the design of a neural network simulator with respect to performance, implementation, and testing.
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