A brain-like learning system with supervised, unsupervised and reinforcement learning

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to journalArticle

Abstract

Our brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. And it is suggested that those learning paradigms relate deeply to the cerebellum, cerebral cortex and basal ganglia in the brain, respectively. Inspired by these knowledge of brain, we present a brain-like learning system with those three different learning algorithms. The proposed system consists of three parts: the supervised learning (SL) part, the unsupervised learning (UL) part and the reinforcement learning (RL) part. The SL part, corresponding to the cerebellum of brain, learns an input-output mapping by supervised learning. The UL part, corresponding to the cerebral cortex of brain, is a competitive learning network, and divides an input space to subspaces by unsupervised learning. The RL part, corresponding to the basal ganglia of brain, optimizes the model performance by reinforcement learning. Numerical simulations show that the proposed brain-like learning system optimizes its performance automatically and has superior performance to an ordinary neural network.

Original languageEnglish
Pages (from-to)15+1165-1172
JournalIEEJ Transactions on Electronics, Information and Systems
Volume126
Issue number9
Publication statusPublished - 2006 Jan 1

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Keywords

  • Brain-like model
  • Neural network
  • Reinforcement learning
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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