Schillen T B
Max-Planck-Institut für Hinforschung, Frankfurt, FRG.
Comput Appl Biosci. 1991 Oct;7(4):417-30. doi: 10.1093/bioinformatics/7.4.417.
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.
近年来，神经网络研究领域越来越吸引来自众多不同学科的工作者的关注。当前的研究主题涵盖了诸多不同方面，如脑生理学中的详细模拟、生物化学中蛋白质结构的预测、计算机科学中的数据库组织，以及各种技术应用。这些不同方法背后的共同模式是使用由简单计算元素组成的分布式网络，这些元素通过加权链接相互通信。神经网络的计算机模拟需要一个合适的软件环境。由于许多此类网络在计算上具有相似性，模拟软件可以构建成模块化组件，在很大程度上独立于特定应用。本文及后续论文的目的是讨论一些与神经网络模拟软件相关的设计考量。所呈现的这些方面对于新模拟软件的开发以及现有程序的有效使用和修改都很有意义。因此，普通用户以及软件设计师有望从这些内容中受益。本文简要介绍了神经网络的一些基本原理。在对不同的软件设计方法进行简短讨论之后，给出了两个简单的示例应用，以展示许多网络模拟共有的概念框架。然后以马克斯 - 普朗克脑研究所开发的MENS网络模拟器为例，说明如何将这些考量应用于模拟软件的设计。本文对数据结构和不同软件组件的布局进行了总体介绍。利用这两个介绍性示例展示了网络分析的一些方面。后续论文将进一步探讨神经网络模拟器在性能、实现和测试方面设计的更多细节。
