多值指数关联联想记忆模型（MMECAM）是一种高存储容量的自联想记忆神经网络。在详细分析其优缺点的基础上，通过改进MMECAM模型的更新规则，首先提出一个新的高斯自联想记忆模型（GAM），然后通过定义简单的能量函数从理论上证明其在同、异步方式下的稳定性，从而保证所存储的模式能最终成为GAM的稳定点；其次，通过引入一般相似性测度进一步提出广义GAM模型（G-GAMs）框架，使得GAM模型成为其特例;最后，将GAM模型应用于单样本图像识别，计算机模拟证实了该模型的鲁棒性能。

Modified Multi-valued Exponential Correlation Associative Memory Model (MMECAM) is a neural network with higher storage capacity. In this paper, based on the analyses of the strengths and weaknesses of MMECAM, a new Gauss Auto-associative Memory Model (GAM) is proposed by modifying its update rule. Then the stability of the proposed GAM is tested in synchronous and asynchronous update modes with a defined energy function, which ensures that the learnt patterns become stable points of the GAM. Further, a framework of Generalized GAM models (G-GAMs) is presented by introducing general similarity measures which makes GAM become its special cases. Finally, the GAM is applied to image recognition from a single sample per image successfully, and the computer simulation results verify GAM’s robust performance.