NDLI: Collective Behavior of a Small-World Recurrent Neural System With Scale-Free Distribution

Content Provider	IEEE Xplore Digital Library
Author	Zhidong Deng Yi Zhang
Copyright Year	1990
Abstract	This paper proposes a scale-free highly clustered echo state network (SHESN). We designed the SHESN to include a naturally evolving state reservoir according to incremental growth rules that account for the following features: (1) short characteristic path length, (2) high clustering coefficient, (3) scale-free distribution, and (4) hierarchical and distributed architecture. This new state reservoir contains a large number of internal neurons that are sparsely interconnected in the form of domains. Each domain comprises one backbone neuron and a number of local neurons around this backbone. Such a natural and efficient recurrent neural system essentially interpolates between the completely regular Elman network and the completely random echo state network (ESN) proposed by Jaeger We investigated the collective characteristics of the proposed complex network model. We also successfully applied it to challenging problems such as the Mackey-Glass (MG) dynamic system and the laser time-series prediction. Compared to the ESN, our experimental results show that the SHESN model has a significantly enhanced echo state property and better performance in approximating highly complex nonlinear dynamics. In a word, this large scale dynamic complex network reflects some natural characteristics of biological neural systems in many aspects such as power law, small-world property, and hierarchical architecture. It should have strong computing power, fast signal propagation speed, and coherent synchronization.
Sponsorship	IEEE Computational Intelligence Society
Page Count	12
File Size	566724
Starting Page	1364
Ending Page	1375
File Format	PDF
ISSN	10459227
Volume Number	18
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Nonlinear dynamical systems Reservoirs Spine Complex networks Laser modes Biological system modeling Power system modeling Large-scale systems Computer architecture time-series prediction Echo state network (ESN) local preferential attachments recurrent neural networks (RNNs) scale-free small world
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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