Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world's future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.

Original languageEnglish
Pages (from-to)34-52
Number of pages19
JournalCognitive Systems Research
Volume68
Early online date17 Feb 2021
DOIs
Publication statusE-pub ahead of print - 17 Feb 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Adaptive network model
  • Criterial causation
  • Informational content
  • Temporal factorisation

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