Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Weakly connected quasi-periodic iscillators, FM interactions, and multiplexing in the brain
SIAM Journal on Applied Mathematics
Cellular neural networks: mosaic pattern and spatial chaos
SIAM Journal on Applied Mathematics
AI Magazine
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Dynamics of periodic delayed neural networks
Neural Networks
Dynamical Behaviors of a Large Class of General Delayed Neural Networks
Neural Computation
Delayed neural networks with multistable almost periodic solutions
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Multistability and new attraction basins of almost-periodic solutions of delayed neural networks
IEEE Transactions on Neural Networks
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A general methodology that involves geometric configuration of the network structure for studying multistability and multiperiodicity is developed. We consider a general class of nonautonomous neural networks with delays and various activation functions. A geometrical formulation that leads to a decomposition of the phase space into invariant regions is employed. We further derive criteria under which the n-neuron network admits 2n exponentially stable sets. In addition, we establish the existence of 2n exponentially stable almost periodic solutions for the system, when the connection strengths, time lags, and external bias are almost periodic functions of time, through applying the contraction mapping principle. Finally, three numerical simulations are presented to illustrate our theory.