Please use this identifier to cite or link to this item:
|Title:||Dynamic stability conditions for Lotka-Volterra recurrent neural networks with delays|
|Citation:||Yi, Z., Tan, K.K. (2002-07). Dynamic stability conditions for Lotka-Volterra recurrent neural networks with delays. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 66 (1) : 011910/1-011910/8. ScholarBank@NUS Repository. https://doi.org/10.1103/PhysRevE.66.011910|
|Abstract:||The Lotka-Volterra model of neural networks, derived from the membrane dynamics of competing neurons, have found successful applications in many "winner-take-all" types of problems. This paper studies the dynamic stability properties of general Lotka-Volterra recurrent neural networks with delays. Conditions for nondivergence of the neural networks are derived. These conditions are based on local inhibition of networks, thereby allowing these networks to possess a multistability property. Multistability is a necessary property of a network that will enable important neural computations such as those governing the decision making process. Under these nondivergence conditions, a compact set that globally attracts all the trajectories of a network can be computed explicitly. If the connection weight matrix of a network is symmetric in some sense, and the delays of the network are in L2 space, we can prove that the network will have the property of complete stability. ©2002 The American Physical Society.|
|Source Title:||Physical Review E - Statistical, Nonlinear, and Soft Matter Physics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2018
WEB OF SCIENCETM
checked on Nov 28, 2018
checked on Nov 16, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.