The Bioenergetic Continuity Framework: Synaptic Downscaling and Open-Loop Generative Mechanisms in Sleep and Dreaming

The Bioenergetic Continuity Framework proposes that altered states of consciousness arise when predictive computation continues while the underlying information substrate is degraded or partially disconnected. Sleep is the canonical biological example. The framework integrates two principles: an evolutionarily conserved metabolic leveling mechanism requiring a homeostatic clearance phase with widespread synaptic downscaling, and the Computational Continuity Hypothesis, which holds that predictive processing continues during clearance. Metabolic clearance preferentially erodes low-mass synaptic microstructures encoding transient daily information, while high-mass pathways remain stabilized by persistent concerns. Predictive computation reconstructs activity from this reduced architecture, and because sensory input is incompletely gated, this intrinsic activity becomes conscious experience.

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Abstract

The Bioenergetic Continuity Framework proposes that altered states of consciousness arise whenever predictive computation continues while the underlying information substrate is degraded or partially disconnected. Sleep is the canonical biological example of this principle. The Bioenergetic Continuity Framework (BCF) provides a unified biological and computational account of this process by linking cellular metabolism, synaptic homeostasis, sleep, dreaming, and altered states of consciousness within a single framework. The BCF rejects the neurocentric view that treats sleep as an adaptation for cognitive optimization. It proposes that sleep emerges from fundamental metabolic constraints, whereas dreaming reflects predictive computation operating on a dynamically restructured neural substrate. The framework integrates two complementary principles. The first is an evolutionarily conserved metabolic levelling mechanism rooted in early prokaryotic isolation strategies (e.g., the KaiABC clockwork). This mechanism requires a homeostatic clearance phase (Hc) in which widespread synaptic downscaling restores energetic equilibrium. The second is the Computational Continuity Hypothesis, which holds that predictive processing continues throughout this clearance phase. Metabolic clearance preferentially erodes low-mass synaptic microstructures encoding transient daily information (Wfine → Wblurred), whereas high-mass pathways remain stabilized by persistent cognitive preoccupations, unresolved behavioral loops, and enduring existential concerns. Predictive computation reconstructs activity from this reduced synaptic architecture. Because sensory input is profoundly but incompletely gated (ε), this intrinsic activity becomes conscious experience. Dream phenomenology therefore reflects the interaction between reduced sensory constraint and diminished synaptic resolution, providing the canonical computational template for altered states of consciousness.