Reinforcement learning algorithm with network extension for pulse neural network

Koichiro Takita, Yuko Osana, Masafumi Hagiwara

Research output: Contribution to journalConference article


In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.

Original languageEnglish
Pages (from-to)2586-2591
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2000 Dec 1
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 2000 Oct 82000 Oct 11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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