Associative dynamics in a chaotic neural network
Neural Networks
A Hierarchical Self-Organizing Map Model for Sequence Recognition
Neural Processing Letters
Pulsed Neural Networks
Associative memory with dynamic synapses
Neural Computation
A Neural Model for Context-dependent Sequence Learning
Neural Processing Letters
Chaotic hopping between attractors in neural networks
Neural Networks
A novel Episodic Associative Memory model for enhanced classification accuracy
Pattern Recognition Letters
Associative memory with a controlled chaotic neural network
Neurocomputing
IEEE Transactions on Computers
Synaptic depression enlarges basin of attraction
Neurocomputing
Neural Computing and Applications
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Temporal information processing, for instance the temporal association, plays an important role on many functions of brain. Among the various dynamics of neural networks, dynamic depression synapses and chaotic behavior have been regarded as the intriguing characteristics of biological neurons. In this paper, temporal association based on dynamic synapses and chaotic neurons is proposed. Interestingly, by introducing dynamic synapses into a temporal association, we found that the sequence storage capacity can be enlarged, that the transition time between patterns in the sequence can be shortened, and that the stability of the sequence can be enhanced. For particular interest, owing to chaotic neurons, the steady-state period becomes shorter in the temporal association and it can be adjusted by changing the parameter values of chaotic neurons. Simulation results demonstrating the performance of the temporal association are presented.