This paper introduces a neural network model that learns multiple attributes as images and performs associated, sequential recall of the learned memories. Briefly, the model presented here is an associative memory model that extends previous models [1] by increasing the number of attributes. In the real world, memory recall generates a chain of associations consisting of complex and diverse data with meaningful relations. However, because this experimental system is designed to implement and verify the processing operations behind such operations, we believe it is not a problem if the associative memory (i.e., the chain of data) is composed of attributes that do not necessarily have clear relation with each other. Accordingly, the attribute-processing systems prepared in this study consist of five types: the C.CB-RN system for processing color attributes, the S.CB-RN system for shape attributes, and the V.CB-RN system for size attributes, as adopted in our previous paper [1], as well as the SV.CB-RN system for processing the names of the world's most beautiful scenery (spectacular view names) and the CN.CB-RN system for processing constellation names. As before, the data presented to each CB-RN system are represented as image patterns using QR codes [2]. These five types of CB-RN systems will be combined and trained with QR code pattern images of the attribute elements of each system. After that, when a pattern image of an attribute element is presented to any of the CB-RN systems, a mechanism will be constructed in which a chain (associative) recall of pattern images of related attribute elements in the other trained systems will be generated.

翻译：本文提出一种神经网络模型，可学习包含多种属性的图像信息并实现已学习记忆的关联性序列回忆。简而言之，本模型通过增加属性数量拓展了先前模型[1]的联想记忆功能。现实生活中，记忆检索会生成由复杂多样且具有语义关联的数据构成的联想链。然而，由于本实验系统旨在实现并验证此类处理机制背后的运算逻辑，即便构成联想记忆（即数据链）的各属性间不存在明确关联关系亦无不可。为此，本研究构建了五种属性处理系统：继承先前研究[1]的色彩属性处理系统C.CB-RN、形状属性处理系统S.CB-RN、尺寸属性处理系统V.CB-RN，以及新增的世界绝景名称属性处理系统SV.CB-RN和星座名称属性处理系统CN.CB-RN。与先前工作相同，各CB-RN系统所处理的数据均以QR码[2]图像模式呈现。通过将这五种CB-RN系统与各属性元素的QR码图像模式进行联合训练后，当任意CB-RN系统接收到某属性元素的模式图像时，即可构建起自动生成其他已训练系统中相关属性元素模式图像链式（联想）回忆的机制。