Hopf bifurcation stability in Hopfield neural networks

In this paper we consider a simple discrete Hopfield neural network model and analyze local stability using the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, the Hopf bifurcation must be determined. For the case of two neurons, we find one neces...

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Bibliographic Details
Published in:Neural networks 2012-12, Vol.36, p.51-58
Main Authors: Marichal, R.L., González, E.J., Marichal, G.N.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Arnold tongue
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Hopf bifurcation
Neural networks
Neural Networks (Computer)
Nonlinear dynamics and nonlinear dynamical systems
Physics
Quasi-periodic orbit
Statistical physics, thermodynamics, and nonlinear dynamical systems
Systems Theory
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Summary:In this paper we consider a simple discrete Hopfield neural network model and analyze local stability using the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, the Hopf bifurcation must be determined. For the case of two neurons, we find one necessary condition that yields the Hopf bifurcation. In addition, we determine the stability and direction of the Hopf bifurcation by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. We analyze the influence of bias weights on the stability of the quasi-periodic orbit and study the phase-locking phenomena for certain experimental results with Arnold Tongues in a particular weight configuration.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2012.09.007