Please use this identifier to cite or link to this item:
Title: Identifying the topology of a coupled FitzHugh-Nagumo neurobiological network via a pinning mechanism
Authors: Zhou, J
Yu, W
Li, X
Small, M
Lu, JA
Keywords: Complex network
Neural network
Topology identification
Weight couplings
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, 2009, v. 20, no. 10, p. 1679-1684 How to cite?
Journal: IEEE transactions on neural networks 
Abstract: Topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology. Different from recent similar works in which the evolution of all the nodes in a complex network need to be received, this brief presents a novel criterion to identify the topology of a coupled FitzHugh-Nagumo (FHN) neurobiological network by receiving the membrane potentials of only a fraction of the neurons. Meanwhile, although incomplete information is received, the evolution of all the neurons including membrane potentials and recovery variables are traced. Based on Schur complement and Lyapunov stability theory, the exact weight configuration matrix can be estimated by a simple adaptive feedback control. The effectiveness of the proposed approach is successfully verified by neural networks with fixed and switching topologies.
ISSN: 1045-9227
DOI: 10.1109/TNN.2009.2029102
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.