In this paper, we present a new neural network model based on attribute-specific representations (e.g., color, shape, size), a classic example of associative memory. The proposed model is based on a previous study on memory and recall of multiple images using the Cue Ball and Recall Net (referred to as the CB-RN system, or simply CB-RN) [1]. The system consists of three components, which are C.CB-RN for processing color, S.CB-RN for processing shape, and V.CB-RN for processing size. When an attribute data pattern is presented to the CB-RN system, the corresponding attribute pattern of the cue neurons within the Cue Balls is associatively recalled in the Recall Net. Each image pattern presented to these CB-RN systems is represented using a two-dimensional code, specifically a QR code [2].

翻译：本文提出了一种基于属性特异性表征（如颜色、形状、尺寸）的新型神经网络模型，作为联想记忆的经典示例。该模型基于先前利用线索球与回忆网络（简称CB-RN系统）进行多图像记忆与召回的研究[1]。系统包含三个组件：处理颜色的C.CB-RN、处理形状的S.CB-RN以及处理尺寸的V.CB-RN。当属性数据模式输入CB-RN系统时，回忆网络中会通过联想机制召回线索球内对应属性的神经元模式。输入这些CB-RN系统的每个图像模式均采用二维编码表示，具体为QR码[2]。