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leiden_hybrid_concepts
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community:leiden_hybrid_concepts-run4-c3-c7Substrate-independent associative learning in biological networks
Learning and memory mechanisms (Pavlovian conditioning, pattern completion) emerge in gene regulatory and molecular networks through coarse-graining and causal emergence analysis.
9 members. Each node is clickable.
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The papers/notes whose extracted claims & findings make up this cluster.
- Generalizing frameworks for sentience beyond natural species2 members
- An association-based model of dynamic behaviour2 members
- Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies2 members
- Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds1 member
- Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds1 member
- AI: A Bridge Toward Diverse Intelligence.md1 member
- 2021-11-09_dorian_schrodinger-life.pdf_c3c4f51 member
Bridges (5)
Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.
Claims (5)
- Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.
- Dimensional Unbounded Space of Related Self-Similar MechanismsPiumarta's central thesis: all diverse dynamic mechanisms (object-oriented, subject-oriented, context-oriented, Worlds, etc.) are specializations of points within a parameterizable multidimensional associative space.
- Gene regulatory networks may exhibit learning capacity.Recent models show GRNs can perform associative learning and pattern completion.
- Genetic information must encode not only physical forms but also complex self-modifying control mechanisms for behavior and development.
- Many apparently different object-oriented, functional, and relational mechanisms are closely related as variations within a general parameterizable n-way associative memory.
Findings (4)
- Biological networks exhibit the lowest EI among real networks and show the most significant causal emergence after coarse-graining.Finding from Klein & Hoel (2020) on real network analysis.
- Gene regulatory network models exhibit associative learning and pattern completion.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 exhibit associative learningEvidence that non-neural systems meet Crump's criterion #7; supports generalization of sentience criteria beyond neural substrates.
- Protein interaction networks across >1800 species exhibit macro-scale nodes with lower noise and higher resilience; eukaryotes show stronger CE than archaea.Klein et al. (2021) analysis of biological interactomes.