Gene regulatory networks can evolve associative memory, storing and recalling multiple phenotypes from partial selective cues (Watson et al. 2010)

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• Gene regulatory networks exhibit associative learning capacity and can be trained via environmental stimuli, not only via genetic rewiring.hypothesis0.892
  Challenges mechanistic view of GRNs; suggests they occupy higher position on persuadability axis than previously assumed.
• Gene regulatory networks exhibit associative learningfinding0.877
  Evidence that non-neural systems meet Crump's criterion #7; supports generalization of sentience criteria beyond neural substrates.
• Gene regulatory network models exhibit associative learning and pattern completion.finding0.861
  Analysis of GRN models shows they can perform several kinds of learning, supporting the view of cellular networks as agents on a cognitive continuum.
• Gene regulatory networks may exhibit learning capacity.claim0.860
  Recent models show GRNs can perform associative learning and pattern completion.
• Associative learning criterion can occur in gene regulatory networks and non-neural morphogenetic agentshypothesis0.849
• Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.claim0.848
• Ecological community networks can evolve associative memory under individual-level selection (Power et al. 2015)finding0.810
  Shows that systems can learn without presupposing system-level unit of selection
• Genetic information must encode not only physical forms but also complex self-modifying control mechanisms for behavior and development.claim0.801