Long term memory storage capacity of multiconnected neural networks
Biological Cybernetics
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
On the stability analysis of delayed neural networks systems
Neural Networks
Multistability of HNNs with almost periodic stimuli and continuously distributed delays
International Journal of Systems Science
Convergence analysis of general neural networks under almost periodic stimuli
International Journal of Circuit Theory and Applications
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Delay-dependent H∞ and generalized H2 filtering for delayed neural networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Mathematical and Computer Modelling: An International Journal
Stability analysis of bidirectional associative memory networks with time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Multiperiodicity of Discrete-Time Delayed Neural Networks Evoked by Periodic External Inputs
IEEE Transactions on Neural Networks
Global Exponential Stability of Multitime Scale Competitive Neural Networks With Nonsmooth Functions
IEEE Transactions on Neural Networks
Dynamics of Winner-Take-All Competition in Recurrent Neural Networks With Lateral Inhibition
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Anti-periodic solutions for high-order neural networks with mixed time delays
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Mathematical and Computer Modelling: An International Journal
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This paper presents new results on multistability of networks when neurons undergo self-excitation and second-order synaptic connectivity. Due to self-excitation of neurons, we split state space into invariant regions and establish new criteria of co-existence of equilibria (periodic orbits) which are exponentially stable. It is shown that high-order synaptic connectivity and external inputs play an important role on the number of equilibria and their convergent dynamics. As a consequence, our results refute traditional viewpoint: high-order interactions of neurons have faster convergence rate and greater storage capacity than first-order ones. Finally, numerical simulations will illustrate our new and interesting results.