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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19373306 |
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| _version_ | 1866901671493238784 |
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| author | Krūger, Marcel |
| author_facet | Krūger, Marcel |
| contents | <p>Recent advances in monitoring biological dynamical systems have demonstrated that regime transitions and systemic instability can be detected early using reduced-dimensional representations, such as Information-Driven Cognitive Entropy (ICE) state spaces. However, translating these predictive diagnostics into active stabilization strategies remains a fundamental challenge. In this work, we introduce a conservative closed-loop intervention mapping framework. Rather than proposing specific mechanistic control laws or clinical therapies, we formulate a structured control-theoretic approach to construct candidate intervention vectors in a reduced state space. Bydefining “Isostasis” as a reference attractor, we establish a mathematically explicit and experimentally falsifiable pathway to test whether stabilizing input–response mappings exist across physiological systems. The framework does not assume controllability or therapeutic efficacy, but instead provides a minimal structure for evaluating intervention consistency under controlled conditions. If empirically validated, this approach may provide a computational foundation for translating abstract control inputs into physically realizable intervention signals, supporting future biofeedback and controlled stabilization environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19373306 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Intervention Mapping in ICE Space: From ∆Φ Detection to Closed-Loop Stabilization Krūger, Marcel <p>Recent advances in monitoring biological dynamical systems have demonstrated that regime transitions and systemic instability can be detected early using reduced-dimensional representations, such as Information-Driven Cognitive Entropy (ICE) state spaces. However, translating these predictive diagnostics into active stabilization strategies remains a fundamental challenge. In this work, we introduce a conservative closed-loop intervention mapping framework. Rather than proposing specific mechanistic control laws or clinical therapies, we formulate a structured control-theoretic approach to construct candidate intervention vectors in a reduced state space. Bydefining “Isostasis” as a reference attractor, we establish a mathematically explicit and experimentally falsifiable pathway to test whether stabilizing input–response mappings exist across physiological systems. The framework does not assume controllability or therapeutic efficacy, but instead provides a minimal structure for evaluating intervention consistency under controlled conditions. If empirically validated, this approach may provide a computational foundation for translating abstract control inputs into physically realizable intervention signals, supporting future biofeedback and controlled stabilization environments.</p> |
| title | Intervention Mapping in ICE Space: From ∆Φ Detection to Closed-Loop Stabilization |
| url | https://doi.org/10.5281/zenodo.19373306 |