Random neural networks with state-dependent firing neurons

被引：6
|
作者：
Jo, SH [1 ]
Yin, HJ
Mao, ZH
机构：
[1] MIT, Dept Elect Engn & Comp Sci, Cambridge, MA 02139 USA
[2] HRL Labs, Malibu, CA 90265 USA
[3] Harvard Univ, MIT, Div Hlth Sci & Technol, Cambridge, MA 02139 USA
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 04期
关键词：
associative memory; random neural networks (RNNs); spiking neurons; state-dependent firing rate;
D O I：
10.1109/TNN.2005.849829
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This letter studies the properties of the random neural networks (RNNs) with state-dependent firing neurons. It is assumed that the times between successive signal emissions of a neuron are dependent on the neuron potential. Under certain conditions, the networks keep the simple product form of stationary solutions and exhibit enhanced capacity of adjusting the probability distribution of the neuron states. It is demonstrated that desired associative memory states can be stored in the networks.
引用
收藏
页码：980 / 983
页数：4
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