Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2015
Abstract
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.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Computational Intelligence and Neuroscience
First Page
1
Last Page
7
ISSN
1687-5265
Identifier
10.1155/2015/191745
Publisher
Hindawi
Citation
LI, Guoqi; RAMANATHAN, Kiruthika; NING, Ning; SHI, Luping; and WEN, Changyun.
Memory dynamics in attractor networks. (2015). Computational Intelligence and Neuroscience. 1-7.
Available at: https://ink.library.smu.edu.sg/sis_research/7386
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1155/2015/191745