Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103083
Title: Memory dynamics in attractor networks
Authors: Li, Guoqi
Ramanathan, Kiruthika
Ning, Ning
Shi, Luping
Wen, Changyun
Keywords: DRNTU::Science::Biological sciences::Human anatomy and physiology::Neurobiology
Issue Date: 2015
Source: Li, G., Ramanathan, K., Ning, N., Shi, L., & Wen, C. (2015). Memory dynamics in attractor networks. Computational intelligence and neuroscience, 2015, 191745-.
Series/Report no.: Computational intelligence and neuroscience
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.
URI: https://hdl.handle.net/10356/103083
http://hdl.handle.net/10220/25812
DOI: 10.1155/2015/191745
Rights: © 2015 Guoqi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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