Metabolic State Modulator Architecture (MSMA)

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Authors: Dead Elvis
Cited by: 0

Abstract

The Metabolic State Modulator Architecture (MSMA) is a theoretical closed-loop control framework derived from the Metabolic Lucidity Model (MLM). While MLM formalizes cognitive clarity and regulated regimes as bounded dynamical states defined by interacting variables (attentional activation, coherence, metabolic resource availability, internal noise, and phase dynamics), it does not specify how such variables might be operationally monitored or conservatively modulated. MSMA addresses this structural gap by translating a dynamical state model into a layered control architecture composed of: • a Sensor Layer acquiring physiological proxies (HRV, EEG, respiration, electrodermal activity, thermodynamic gradients),• a nonlinear State Estimation Layer implementing multi-timescale signal fusion and probabilistic filtering,• a constraint-aware Control Core employing predictive control under energetic and stability limits,• and a low-intensity Actuation Layer providing non-invasive phase-aligned modulation. The architecture explicitly incorporates energetic envelope constraints, partial observability, nonlinear sensitivity near bifurcation boundaries, and individual parameter variability. It does not claim deterministic induction of cognitive states, full controllability, clinical applicability, or therapeutic outcomes. Instead, it defines bounded trajectory shaping within physiologically admissible regions of state-space. MSMA should be interpreted as a formal blueprint rather than a validated device model. Its contribution is architectural: demonstrating how a nonlinear cognitive state formalism can be expressed as a conservative closed-loop control system under strict energetic and ethical constraints. Version: v1.0

Fields of Study & Subjects

Cognitive Science and Education Research Psychiatry Mental Health Neuroscience Neural dynamics and brain function

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
26 February 2026
Language
EN
Document type
Preprint