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.