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

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)32-39
Number of pages8
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume162
Issue number1
DOIs
Publication statusPublished - 2008 Jan 15

Keywords

  • Brainlike model
  • Neural networks
  • Reinforcement learning
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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