@article{oai:tohoku.repo.nii.ac.jp:00021909,
 author = {HOMMA, Noriyasu and GUPTA, Madan M.},
 issue = {2},
 journal = {東北大学医療技術短期大学部紀要 = Bulletin of College of Medical Sciences, Tohoku University},
 month = {Jul},
 note = {application/pdf, We propose a novel neural network for incremental learning tasks where networks are required to learn new knowledge without forgetting the old one. An essential core of the proposed neural learning structure is a transferring scheme from short-term memory (STM) into long-term memory (LTM) as in brains by using dynamic changing weights. As the number of LTMs increases, a new network structure is superimposed on the previous one without disturbing the past LTMs by introducing a lateral inhibition mechanism. Superiority of the proposed neural structure to the conventional backpropagation networks is proven with respect to the learning ability., 紀要類(bulletin), 671882 bytes},
 pages = {111--120},
 title = {Memory Superimposition by Backpropagation Neural Networks},
 volume = {12},
 year = {2003}
}