Bri CA: A modular software platform for whole brain architecture

Kotone Itaya, Koichi Takahashi, Masayoshi Nakamura, Moriyoshi Koizumi, Naoya Arakawa, Masaru Tomita, Hiroshi Yamakawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Brain-inspired Computing Architecture (Bri CA) is a generic software platform for modular composition of machine learning algorithms. It can combine and schedule an arbitrary number of machine learning components in a brain-inspired fashion to construct higher level structures such as cognitive architectures. We would like to report and discuss the core concepts of Bri CA version 1 and prospects toward future development.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
PublisherSpringer Verlag
Pages334-341
Number of pages8
Volume9947 LNCS
ISBN (Print)9783319466866
DOIs
Publication statusPublished - 2016
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 2016 Oct 162016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9947 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd International Conference on Neural Information Processing, ICONIP 2016
CountryJapan
CityKyoto
Period16/10/1616/10/21

Keywords

  • Cognitive architecture
  • Machine learning
  • Modularity
  • Software platform
  • The whole brain architecture

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Itaya, K., Takahashi, K., Nakamura, M., Koizumi, M., Arakawa, N., Tomita, M., & Yamakawa, H. (2016). Bri CA: A modular software platform for whole brain architecture. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (Vol. 9947 LNCS, pp. 334-341). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46687-3_37