Simulation of chaotic EEG patterns with a dynamic model of the olfactory system
Biological Cybernetics
Associative dynamics in a chaotic neural network
Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A transient-chaotic autoassociative network (TCAN) based on Lee oscillators
IEEE Transactions on Neural Networks
A functional model of limbic system of brain
BI'09 Proceedings of the 2009 international conference on Brain informatics
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To realize mutual association function, we propose a hippoca- mpus-neocortex model with multi-layered chaotic neural network (MCNN). The model is based on Ito etal.'s hippocampus-cortex model (2000), which is able to recall temporal patterns, and form long-term memory. The MCNN consists of plural chaotic neural networks (CNNs), whose each CNN layer is a classical association model proposed by Aihara. MCNN realizes mutual association using incremental and relational learning between layers, and it is introduced into CA3 of hippocampus. This chaotic hippocampus-neocortex model intends to retrieve relative multiple time series patterns which are stored (experienced) before when one common pattern is represented. Computer simulations verified the efficiency of proposed model.