期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 21, 期 10, 页码 1690-1695出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2010.2067224
关键词
Continuous attractors; Lotka-Volterra recurrent neural networks; stable; unstable
类别
资金
- National Science Foundation of China [60970013]
- National Basic Research Program of China under Program 973 [2011CB302201]
- Fundamental Research Funds for the Central Universities [ZYGX2009J102]
Continuous attractors of Lotka-Volterra recurrent neural networks (LV RNNs) with infinite neurons are studied in this brief. A continuous attractor is a collection of connected equilibria, and it has been recognized as a suitable model for describing the encoding of continuous stimuli in neural networks. The existence of the continuous attractors depends on many factors such as the connectivity and the external inputs of the network. A continuous attractor can be stable or unstable. It is shown in this brief that a LV RNN can possess multiple continuous attractors if the synaptic connections and the external inputs are Gussian-like in shape. Moreover, both stable and unstable continuous attractors can coexist in a network. Explicit expressions of the continuous attractors are calculated. Simulations are employed to illustrate the theory.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据