@article{(Open Science Index):https://publications.waset.org/pdf/9996986,
	  title     = {Dual-Network Memory Model for Temporal Sequences},
	  author    = {Motonobu Hattori and  Rina Suzuki},
	  country	= {},
	  institution	= {},
	  abstract     = {In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This
drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudopatterns. Because temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional  dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.
 
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {12},
	  year      = {2013},
	  pages     = {1602 - 1606},
	  ee        = {https://publications.waset.org/pdf/9996986},
	  url   	= {https://publications.waset.org/vol/84},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 84, 2013},
	}