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Temporal association based on dynamic depression synapses and chaotic neurons |
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| Authors: | Min XiaAuthor Vitae Zhijie WangAuthor VitaeJian’an FangAuthor Vitae |
| Affiliation: | a College of Information and Control Science, Nanjing University of Information Science and Technology, Nanjing, China b College of Information Science and Technology, Donghua University, Shanghai, China c Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong |
| 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. |
| Keywords: | Temporal association Dynamic synapse Chaotic neuron Steady-state period Sequence storage capacity Transition time |
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