finding
active
finding:gene-regulatory-networks-exhibit-associative-learningGene regulatory networks exhibit associative learning
Evidence that non-neural systems meet Crump's criterion #7; supports generalization of sentience criteria beyond neural substrates.
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Communities (3)
community
- Causal emergence in biological systemsmembers_ofExamines how macro-scale causal power exceeds micro-scale in living and learning systems.
- Learning and memory mechanisms (Pavlovian conditioning, pattern completion) emerge in gene regulatory and molecular networks through coarse-graining and causal emergence analysis.
- Non-neural associative learning in GRNsmembers_ofGene regulatory networks exhibit Pavlovian conditioning and pattern completion via deterministic molecular dynamics.
Findings (1)
finding
- Analysis of GRN models shows they can perform several kinds of learning, supporting the view of cellular networks as agents on a cognitive continuum.
Frameworks (1)
framework
- Quantitative, operationalizable criteria proposed by Crump et al. that Levin argues can be generalized beyond natural species.
Related by similarity (8)
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- Challenges mechanistic view of GRNs; suggests they occupy higher position on persuadability axis than previously assumed.
- Recent models show GRNs can perform associative learning and pattern completion.
- Demonstrates information integration in evolutionary systems with system-level selection
- Systems of molecular regulation exhibiting associative learning and downward causation; example of misplaced mechanistic assumptions.
Cross-corpus bridges (3)
same_concept_as · Nomic cosineExternal markdown files that talk about the same concept as this entity.
- aboutblank_kbCan Gene Regulatory Networks be trained and modified through associative learning approaches?questions/can-gene-regulatory-networks-be-trained-and-modified.md0.883
- aboutblank_kbGene Regulatory Networksconcepts/biology/gene-regulatory-networks.md0.841
- aboutblank_kbAssociative Learningconcepts/ai/reinforcement-learning.md0.782
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cosine ≥ 0.90Other entities that say roughly the same thing. May be merge candidates or independent restatements across papers.