Hardware implementation of a DBM network with non-monotonic neurons

Mitsunaga Kinjo, Shigeo Sato, Koji Nakajima

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper, we report a study on hardware implementation of a Deterministic Boltzmann Machine (DBM) with non-monotonic neurons (non-monotonic DBM network). The hardware DBM network has fewer components than other neural networks. Results from numerical simulations show that the non-monotonic DBM network has high learning ability as compared to the monotonic DBM network. These results show that the non-monotonic DBM network has large potential for the implementation of a high functional neurochip. Then, we design and fabricate a neurochip of the non-monotonic DBM network of which measurement confirms that the high-functional large-scale neural system can be realized on a compact neurochip by using the non-monotonic neurons.

Original languageEnglish
Pages (from-to)558-567
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number3
Publication statusPublished - 2002 Mar


  • Analog circuit
  • DBM learning
  • Neural network
  • Neurochip
  • Non-monotonic

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


Dive into the research topics of 'Hardware implementation of a DBM network with non-monotonic neurons'. Together they form a unique fingerprint.

Cite this