Memory Dynamics in Attractor Networks
Joint Authors
Ramanathan, Kiruthika
Ning, Ning
Shi, Luping
Wen, Changyun
Li, Guoqi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-19
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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 designedbased 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, canbe avoided.
The simulation results show the effectiveness of the proposed method.
American Psychological Association (APA)
Li, Guoqi& Ramanathan, Kiruthika& Ning, Ning& Shi, Luping& Wen, Changyun. 2015. Memory Dynamics in Attractor Networks. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057671
Modern Language Association (MLA)
Li, Guoqi…[et al.]. Memory Dynamics in Attractor Networks. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1057671
American Medical Association (AMA)
Li, Guoqi& Ramanathan, Kiruthika& Ning, Ning& Shi, Luping& Wen, Changyun. Memory Dynamics in Attractor Networks. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057671
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057671