A brainlike learning system with supervised, unsupervised, and reinforcement Learning

Takafumi Sasakawa*, Jinglu Hu, Kotaro Hirasawa


研究成果: Article査読

10 被引用数 (Scopus)


According to Hebb's cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that in the brain there are three different learning paradigms: supervised, unsupervised, and reinforcement learning, which are related deeply to the three parts of brain: cerebellum, cerebral cortex, and basal ganglia, respectively. Inspired by the above knowledge of the brain in this paper we present a brainlike learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part, and reinforcement learning (RL) part. The SL part is a main part learning inputoutput mapping; the UL part is a competitive network dividing input space into subspaces and realizes the capability of function localization by controlling firing strength of neurons in the SL part based on input patterns; the RL part is a reinforcement learning scheme, which optimizes system performance by adjusting the parameters in the UL part. Numerical simulations have been carried out and the simulation results confirm the effectiveness of the proposed brainlike learning system.

ジャーナルElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
出版ステータスPublished - 2008 1月 15

ASJC Scopus subject areas

  • エネルギー工学および電力技術
  • 電子工学および電気工学


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