Introduction to the theory of neural computation
Introduction to the theory of neural computation
Extraction of patterns from a hippocampal network using chaotic recall
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
Avoiding catastrophic forgetting by a biologically inspired dual-network memory model
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Neural networks encounter serious catastrophic forgetting when information is learned sequentially, which is unacceptable for both a model of human memory and practical engineering applications. In this study, we propose a novel biologically inspired dual-network memory model that can significantly reduce catastrophic forgetting. The proposed model consists of two distinct neural networks: hippocampal and neocortical networks. Information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network. In the hippocampal network, chaotic behavior of neurons in the CA3 region of the hippocampus and neuronal turnover in the dentate gyrus region are introduced. Chaotic recall by CA3 enables retrieval of stored information in the hippocampal network. Thereafter, information retrieved from the hippocampal network is interleaved with previously stored information and consolidated by using pseudopatterns in the neocortical network. The computer simulation results show the effectiveness of the proposed dual-network memory model.