Modified Hebbian algorithm for analog VLSI neural network implementation

Hiroyuki Wasaki, Yoshihiko Horio, Shogo Nakamura

Research output: Contribution to journalArticlepeer-review


Various studies on learning rules for neural networks have been done However, most of those do not consider the hardware implementation, which is a great drawback in the LSI implementation of neural networks with learning capability. From such a viewpoint, this paper proposes a self organizing learning rule by modifying the Hebbian learning rule. This rule can be implemented easily on an analog VLSI chip as an on-chip learning rule. The self-organizing ability of the system is verified by simulation experiments. It is shown from the experiment that the learning speed is improved by a factor of 2 to 3, and it is possible to avoid the sudden termination of the learning and the divergence of the synaptic weights.

Original languageEnglish
Pages (from-to)20-29
Number of pages10
JournalElectronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number11
Publication statusPublished - 1993 Nov


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