Attractor neural networks and biological reality: associative memory
Future Generation Computer Systems
Effect of connectivity in an associative memory model
Journal of Computer and System Sciences
Connectivity and complexity: the relationship between neuroanatomy and brain dynamics
Neural Networks - Special issue on the global brain: imaging and modelling
IEEE Transactions on Computers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robustness in neural computation: random graphs and sparsity
IEEE Transactions on Information Theory
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A novel sparsely-connected neural network for sequence memory with controllable steady-state period is proposed in this study. By introducing a new exponential kernel sampling function and the sampling interval parameter, the steady-state period can be controlled, and the steady-state time steps is equal to the sampling interval parameter. Ascribing to the exponential kernel sampling function, the sequence storage capacity is enlarged compared with the existing sequence memory models. Owning to the sparsely-connected of Gaussian distribution, the model produces the efficient use of synapse resources, but the sequence storage capacity is decreased compared with the fully-connected networks. The study also gives a significant result that the networks of different dimensions have the same synapse connection efficiency if they are with the same connection mean degree.