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Hebbian learning rule restraining catastrophic forgetting in pulse neural network
Author(s) -
Motoki Makoto,
Hamagami Tomoki,
Koakutsu Seiichi,
Hirata Hironori
Publication year - 2005
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.10343
Subject(s) - forgetting , hebbian theory , learning rule , artificial neural network , computer science , artificial intelligence , competitive learning , point (geometry) , mathematics , psychology , geometry , cognitive psychology
In this paper, a Hebbian learning rule restraining “catastrophic forgetting” is proposed on a pulsed neural network (PNN) with leaky integrate‐and‐fire neurons. The strong point of this learning rule is that a learning of new pattern does not destroy past ones, and that an efficient use of synapses is enabled. First, in order to consider the function of the learning rule, a fundamental experiment is carried out. Next, to compare the performance between the proposed learning rule and conventional ones on the application, simulation experiments are examined using autonomous behavior robots which are forced to learn concurrently two different environments. The results of the experiments show that the proposed learning rule clearly restrains “catastrophic forgetting” and enables working of more efficient than conventional PNN learning. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 50–60, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10343