Memory Dynamics in Attractor Networks

作者：Li Guoqi^*; Ramanathan Kiruthika; Ning Ning; Shi Luping; Wen Changyun

来源：Computational Intelligence and Neuroscience, 2015, 2015: 191745.

DOI：10.1155/2015/191745

As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented to the network, and its states converge to one of stable equilibrium points. Consequently, the existence of the spurious points, that is, local maxima, saddle points, or other local minima which are undesired memory patterns, can be avoided. The simulation results show the effectiveness of the proposed method.

• 出版日期2015
• 单位清华大学; 南阳理工学院