ArticleCommunications biology2025
Associative conditioning in gene regulatory network models increases integrative causal emergence.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- The integrated information Φ of an integrate and fire network.PLoS computational biology · 2026Article
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A reframed landscape of causal emergence.Patterns (New York, N.Y.) · 2026Article
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How does learning affect the integration of an agent's internal components into an emergent whole? We analyzed gene regulatory networks, which learn to associate distinct stimuli, using causal emergence, which captures the degree to which an integrated system is more than the sum of its parts. Analyzing 29 biological (experimentally derived) networks before, during, after training, we discovered that biological networks increase their causal emergence due to training. Clustering analysis uncovered five distinct ways in which networks' emergence responds to training, not mapping to traditional ways to characterize network structure and function but correlating to different biological categories. Our analysis reveals how learning can reify the existence of an agent emerging over its parts and suggests that this property is favored by evolution. Our data have implications for the scaling of diverse intelligence, and for a biomedical roadmap to exploit these remarkable features in networks with relevance for health and disease.
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Registered trials
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