The application of connectionist models also extends into the domain of evolutionary systems, where the connections of a network are changed by variation and selection.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Connectionist learning principles apply equally to evolutionary systems where variation and selection alter network organization, instantiating distributed learning at evolutionary timescales.hypothesis0.857
• Connectionist Approach To Evolutionary Transitions In Individualityframework0.819
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.812
  Central claim about the power of connectionism.
• The ability to represent relationships (e.g. correlations or associations) among variables, rather than a system of independent variables, is essential for any connectionist model of cognition.claim0.801
  Necessary condition for connectionist cognition.
• Evolutionary Connectionismframework0.795
  Proposed framework translating connectionist learning principles into natural selection domain to explain ETIs.
• Can The Principles Of Connectionist Learning And Evolutionquestion0.792
• Connectionist Models of Cognition and Learningframework0.791
  Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
• Connectionist Models Of Cognitionframework0.785