Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neucom.2011.05.009 | Neurocomputing |
Keywords | Field | DocType |
various dynamic,temporal information processing,dynamic depression synapsis,chaotic neuron,sequence storage capacity,steady-state period,transition time,temporal association,important role,dynamic synapse,chaotic behavior,biological neuron,dynamic synapsis,neural network,steady state | Synapse,Information processing,Pattern recognition,Transition time,Artificial intelligence,Artificial neural network,Chaotic,Mathematics | Journal |
Volume | Issue | ISSN |
74 | 17 | Neurocomputing |
Citations | PageRank | References |
5 | 0.48 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Xia | 1 | 52 | 6.70 |
| Zhijie Wang | 2 | 89 | 11.14 |
| Jian'an Fang | 3 | 80 | 6.91 |