Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evolving autonomous learning in cognitive networks
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
2017 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 7, no 1, article id 16712Article in journal (Refereed) Published
Abstract [en]

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines. © 2017 The Author(s).

Place, publisher, year, edition, pages
Nature Publishing Group , 2017. Vol. 7, no 1, article id 16712
Keywords [en]
brain, human, human experiment, logic, machine learning, article
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:du-37171DOI: 10.1038/s41598-017-16548-2Scopus ID: 2-s2.0-85036656005OAI: oai:DiVA.org:du-37171DiVA, id: diva2:1557899
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2022-09-15Bibliographically approved

Open Access in DiVA

fulltext(1804 kB)137 downloads
File information
File name FULLTEXT01.pdfFile size 1804 kBChecksum SHA-512
2a4e1d0b4b3c59bc2074edcfaad9a214bae8189627b1cf6163a620de118824f1e56f76e8d9147c6892b8be50582625762631c135639a21dee4adfdee45a33d51
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Hintze, Arend

Search in DiVA

By author/editor
Hintze, Arend
In the same journal
Scientific Reports
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 137 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 80 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf