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The evolution of representation in simple cognitive networks
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
2013 (English)In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 25, no 8, p. 2079-2107Article in journal (Refereed) Published
Abstract [en]

Representations are internalmodels of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior.We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks-an artificial neural network and a network of hiddenMarkov gates-to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success. © 2013 Massachusetts Institute of Technology.

Place, publisher, year, edition, pages
2013. Vol. 25, no 8, p. 2079-2107
Keywords [en]
algorithm, animal, artificial neural network, cognition, computer simulation, evolution, human, information science, letter, perception, physiology, probability, Algorithms, Animals, Biological Evolution, Humans, Information Theory, Markov Chains, Neural Networks (Computer)
National Category
Evolutionary Biology Computer Sciences
Identifiers
URN: urn:nbn:se:du-37187DOI: 10.1162/NECO_a_00475Scopus ID: 2-s2.0-84880991678OAI: oai:DiVA.org:du-37187DiVA, id: diva2:1557842
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2021-05-27Bibliographically approved

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Hintze, Arend

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CiteExportLink to record
Permanent link

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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