ArticleProceedings of the National Academy of Sciences of the United States of America2024
Minimal motifs for habituating systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- Mathematical modeling for a primitive form of habituation in an amoeba.Journal of mathematical biology · 2026Article
- Dynamical modeling of individual sensory reactivity and habituation learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Engineering Basal Cognition: Minimal Genetic Circuits for Habituation, Sensitization, and Massed-Spaced Learning.ACS synthetic biology · 2026Article
- Article
- A receptor-inactivation model for single-celled habituation in Stentor coeruleus.Current biology : CB · 2025Article
- Minimal motifs for habituating systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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3 authors.
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Abstract
Habituation-a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld-is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.
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