Modeling and Analysis of Associative Memory Networks (EIA)

联想存储器是由具有数据存储能力的相互连接的元素组成的集合。这些元素是通过数据探测向量的内容而不是通过特定地址并行访问的。最近，被称为Hopfield AMN的模型受到了广泛关注。这些模型易于分析，并表现出神经网络的一些行为特征。为了整合这些特征，我们将探索一些新的模型，这些模型保留了Hopfield人工神经网络的简单结构，使它们易于分析。目的是为了更好地了解各种类型的AMN的性质和行为。首席研究员正在分析几种新模型的动态和信息存储能力。许多新模型都受到一定的物理约束和互联成本，将各种网络设计和分析问题表述为优化问题是合适的。这项研究将对未来在计算机体系结构中使用人工神经网络具有重要意义，与信号处理、通信、控制和人工智能等具有挑战性的问题相匹配。理解各种AMN参数与其信息存储容量之间的关系，是确定这种形式的并行分布式计算最有用的方面的一步。

Associative memories are composed of a collection of interconnected elements having data storage capabilites. The elements are accessed in parallel by the content of a data probe vector rather than by a specific address. Recently, models referred to as the Hopfield AMN have received much attention. These models are simple to analyze and exhibit some of the behavioral features of neural networks. To incorporate these features, some new models will be explored which retain enough of the simple structure of the Hopfield AMN that makes them amenable to analysis. The objective is to obtain a better understanding of the properties and behavior of various types of AMN. The principal investigator is analyzing the dynamic and information storage capabilities of several new models. Many of the new models are subject to some physical constraints and interconnection costs, and it is appropriate to formulate various network design and analysis problems as optimization problems. The research will have important implications for the use of AMN in computer architectures of the future, matched to challenging problems in signal processing, communications, control, and artificial intelligence. Understanding the relationship between parameters of various AMN and their information storage capacities is a step towards determining the most useful aspects of this form of parallel distributed computing.