Article
Grouping synchronization in a pulsecoupled network of chaotic spiking oscillators.
Dept. of Comput. Sci. and Media Eng., Musashi Inst. of Technol., Tokyo, Japan.
IEEE Transactions on Neural Networks (Impact Factor: 2.95). 10/2004; 15(5):101826. DOI: 10.1109/TNN.2004.832807 Source: PubMed

Conference Paper: Synchronization of hopfield like chaotic neural networks with structure based learning
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ABSTRACT: In this paper the issue of structure based learning of Hopfield like chaotic neural networks is investigated in such a way that all neurons behave in a synchronous manner. By utilizing the idea of structured inverse eigenvalue problem and the sufficient conditions on the coupling weights of a network which guarantee the synchronization of all neuron's outputs, we propose a learning method for tuning the coupling weights of a network where not only synchronize all neuron's outputs with each other but also brings about any desirable topology for the structure of the network. Specifically, this method is evaluated by performing simulations on the scalefree topology.Proceedings of the 19th international conference on Neural Information Processing  Volume Part II; 11/2012 
Article: Complicated superstable periodic behavior in piecewise constant circuits with impulsive excitation
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ABSTRACT: This paper studies piecewise constant circuits with an impulsive switch. Since vector field of the circuit equation is piecewise constant, the trajectories are piecewise linear : it is well suited for precise analysis. First, we consider the autonomous case. The switch is controlled by a state and the circuits can exhibit chaotic behavior. Second, we consider the nonautonomous case. The switch is controlled by time and the circuits can exhibit rich super stable periodic behavior. We have confirmed the behavior in numerical simulations and embedded return maps have a flat part. Typical phenomena can be confirmed experimentally. 
Article: Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure
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ABSTRACT: In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron’s outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scalefree topology.Cognitive Neurodynamics 04/2014; · 1.77 Impact Factor
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