hypothesis
active
prediction:gene-regulatory-networks-exhibit-associative-learning-capacity-and-can-be-trained-via-environmental-stimuli-not-only-via-genetic-rewiringGene regulatory networks exhibit associative learning capacity and can be trained via environmental stimuli, not only via genetic rewiring.
Challenges mechanistic view of GRNs; suggests they occupy higher position on persuadability axis than previously assumed.
Source paper
extracted_from(2022) · Levin, Michael
Neighborhood — ranked by edge-count
Concepts (1)
concept
- Systems of molecular regulation exhibiting associative learning and downward causation; example of misplaced mechanistic assumptions.
Frameworks (1)
framework
- Axis Of Persuadabilityassociated_withPractical engineering framework for determining optimal level of control for a given system, from brute force to rational argument.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Evidence that non-neural systems meet Crump's criterion #7; supports generalization of sentience criteria beyond neural substrates.
- Demonstrates information integration in evolutionary systems with system-level selection
- Analysis of GRN models shows they can perform several kinds of learning, supporting the view of cellular networks as agents on a cognitive continuum.
- Recent models show GRNs can perform associative learning and pattern completion.
Cross-corpus bridges (2)
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.md0.906
- aboutblank_kbGene Regulatory Networksconcepts/biology/gene-regulatory-networks.md0.859