日本神経回路学会誌
Online ISSN : 1883-0455
Print ISSN : 1340-766X
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4 巻 , 3 号
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  • 塚田 稔
    4 巻 (1997) 3 号 p. 126-135
    公開日: 2010/12/13
    ジャーナル フリー
    There are fundamental differences in the memory system between the brain and that of digital computer. The computer stores and retrieves memories guided by an address information, whereas the brain stores memories over the weight space of neural networks through learning and retrieves them according to some dynamical processes. In the course of establishing long-term memory, hippocampus plays an important role, i. e., to make short-term memory of spatially and temporally associated input information. We (Tsukada, et al., 1996) proposed a spatiotemporal learning rule based on the difference in hippocampal long-term potentiation (LTP) induced by various spatio-temporal pattern stimuli. Essential point of our learning rule is that the synaptic weight changes depending on both “spatial coincidence” and “time history” of input pulses. We compared the pattern discriminating ability of this rule with that of Hebbian and its extention rule, through computer simulation on the one layer neural network model with 24 spatio-temporal input patterns and 120 output neurons. It is shown that the proposed rule has the highest ability in separating different spatio-temporal patterns into the synaptic weight space.
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