Detecting the Earliest Recursive Regime Transition in Neural Systems
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| Formato: | Recurso digital |
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2026
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| _version_ | 1866901580826017792 |
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| author | Thomas, Charles S. |
| author_facet | Thomas, Charles S. |
| contents | <p><span><span><span>Current empirical approaches to consciousness rely on measures of integration, responsiveness, and reportability, all of which track relatively late-stage phenomena. This paper introduces a minimal operational criterion for detecting an earlier event: the first sustained instance of recursive closure in neural dynamics. We argue that recursion functions as a constraint-satisfying mechanism whose irreducible role is identity maintenance — the preservation of a system’s configuration through time via self-referential closure. The proposed “spark” is defined as the earliest time interval in which three conditions co-occur: re-entry (state dependence on immediate past), persistence (non-fragmenting continuity), and configuration-relative comparison (path-dependent evaluation of current state against prior internal configuration). Recursive closure is treated as a binary event that may occur transiently before stabilizing into a regime. Two onset markers are distinguished: T₀ (first closure instance, possibly brief) and T* (first sustained closure exceeding stability threshold τ). Crucially, τ is not a free parameter but is anchored to intrinsic system timescales. The resulting onset timestamps are evaluated using lead–lag tests against established markers such as large-scale integration and behavioral responsiveness.</span></span></span></p> <p><span><span><span>The framework includes a pre-registered analysis protocol specifying all parameter choices, proxy thresholds, and decision rules in advance, together with a practical application guide addressing use cases from clinical anesthesiology to computational model validation.</span></span></span></p> <p><span><span><span>This framework is explicitly agnostic with respect to phenomenological interpretation and provides a constrained, falsifiable method for identifying the earliest detectable recursive regime in neural systems.</span></span></span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19142125 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Detecting the Earliest Recursive Regime Transition in Neural Systems Thomas, Charles S. recursive dynamics neural time series regime transition onset detection self-referential closure identity maintenance anesthesia EEG change-point analysis dynamical systems consciousness (operational) <p><span><span><span>Current empirical approaches to consciousness rely on measures of integration, responsiveness, and reportability, all of which track relatively late-stage phenomena. This paper introduces a minimal operational criterion for detecting an earlier event: the first sustained instance of recursive closure in neural dynamics. We argue that recursion functions as a constraint-satisfying mechanism whose irreducible role is identity maintenance — the preservation of a system’s configuration through time via self-referential closure. The proposed “spark” is defined as the earliest time interval in which three conditions co-occur: re-entry (state dependence on immediate past), persistence (non-fragmenting continuity), and configuration-relative comparison (path-dependent evaluation of current state against prior internal configuration). Recursive closure is treated as a binary event that may occur transiently before stabilizing into a regime. Two onset markers are distinguished: T₀ (first closure instance, possibly brief) and T* (first sustained closure exceeding stability threshold τ). Crucially, τ is not a free parameter but is anchored to intrinsic system timescales. The resulting onset timestamps are evaluated using lead–lag tests against established markers such as large-scale integration and behavioral responsiveness.</span></span></span></p> <p><span><span><span>The framework includes a pre-registered analysis protocol specifying all parameter choices, proxy thresholds, and decision rules in advance, together with a practical application guide addressing use cases from clinical anesthesiology to computational model validation.</span></span></span></p> <p><span><span><span>This framework is explicitly agnostic with respect to phenomenological interpretation and provides a constrained, falsifiable method for identifying the earliest detectable recursive regime in neural systems.</span></span></span></p> |
| title | Detecting the Earliest Recursive Regime Transition in Neural Systems |
| topic | recursive dynamics neural time series regime transition onset detection self-referential closure identity maintenance anesthesia EEG change-point analysis dynamical systems consciousness (operational) |
| url | https://doi.org/10.5281/zenodo.19142125 |