Guest C C, Tekolste R
Appl Opt. 1987 Dec 1;26(23):5055-60. doi: 10.1364/AO.26.005055.
The bidirectional associative memory (BAM) is a powerful neural network paradigm that is well suited to optical implementation. The BAM is heteroassociative (of which autoassociative operation is a special case) and is guaranteed to converge to a stable final state regardless of the connection weight matrix used. The BAM is placed in a conceptual framework that facilitates comparison with other neural network models. Variations on the BAM such as the bidirectional optimal memory (BOM), the competitive BAM (CBAM), and the adaptive BAM (ABAM) indicate some of the interesting directions this simple structure may evolve, leading in a natural progression toward the power of a model such as the Carpenter-Grossberg ART. The simplicity of the BAM invites uncomplicated optical implementations. BAM designs based on optical matrix-vector multipliers (MVMs) and on volume holographic connections are presented. Spatial light modulator (SLM) device designs currently under development to support the MVM BAMs are given.
双向联想记忆（BAM）是一种强大的神经网络范式，非常适合光学实现。BAM是异联想的（自联想操作是其特殊情况），并且无论使用何种连接权重矩阵，都能保证收敛到稳定的最终状态。BAM被置于一个便于与其他神经网络模型进行比较的概念框架中。BAM的变体，如双向最优记忆（BOM）、竞争BAM（CBAM）和自适应BAM（ABAM），表明了这种简单结构可能演变的一些有趣方向，自然地朝着诸如卡彭特 - 格罗斯伯格ART等模型的强大功能发展。BAM的简单性使得其光学实现并不复杂。文中介绍了基于光学矩阵 - 向量乘法器（MVM）和体全息连接的BAM设计。还给出了目前正在开发的用于支持MVM BAM的空间光调制器（SLM）器件设计。
