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Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
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2014 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 12Article in journal (Refereed) Published
Abstract [en]

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity. © 2014 Albantakis et al.

Place, publisher, year, edition, pages
Public Library of Science , 2014. Vol. 10, no 12
Keywords [en]
animat, Article, biomechanics, brain development, conceptual framework, environmental exposure, environmental factor, evolutionary adaptation, information processing, memory, nerve cell network, neuroanatomy, problem solving, sensory nerve, task performance, adaptation, algorithm, biological model, biology, computer simulation, evolution, feedback system, genetic selection, nonparametric test, reproductive fitness, Adaptation, Physiological, Algorithms, Biological Evolution, Computational Biology, Feedback, Physiological, Genetic Fitness, Models, Neurological, Selection, Genetic, Statistics, Nonparametric
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Evolutionary Biology
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URN: urn:nbn:se:du-37186DOI: 10.1371/journal.pcbi.1003966Scopus ID: 2-s2.0-84919628618OAI: oai:DiVA.org:du-37186DiVA, id: diva2:1557836
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
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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
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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