Connectome-as-Operating-System (COS): A Constrained Structural Operator Architecture for Cognitive Differentiation

This paper presents the Connectome-as-Operating-System (COS) architecture, a structural framework for cognitive differentiation in which a fixed biological connectome topology functions as a frozen constraint operator over a shared knowledge base. The central claim is that different structural topologies, applied to the same knowledge base, produce stable and measurable differences in output behavior through the comparison of prior accumulated state against current structurally constrained input — without training, learning, or structural modification.

The architecture separates system function into three independently governed layers: Hard Memory (HM), a mutable knowledge base; Interpretive Memory (IM), a frozen structural operator derived from connectome topology; and Current Load Memory (CLM), an ephemeral session context buffer. This three-node separation maps directly onto the co-dependent architecture established in Baggs (2026), in which consciousness is modeled as awareness (CLM), intelligence (HM), and valuation (IM) mediated by a reconstructive gradient. The current paper operationalizes the valuation component as a frozen structural operator and provides a staged experimental validation.

Update April 27 2026

v1.6 adds the following over v1.6:

Section 5.4: Control Capacity (Non-Operational) — formal definition linking COS gradient dominance to modal control in network neuroscience, with Stage 3B empirical grounding. Ring interneurons (degree 152) show below-average gradient; posterior ganglia show p_adjusted < 0.0001. Not concept padding — independently produced by the data.

Section 8: Stage ordering corrected (0, 1, 2, 3, 3B, 4, 5, 6). Stage 4 redefined as Scale + Synaptic Type Constraint. Stage 3B to Stage 4 bridge sentence added. Stage 4 pre-registered stability risk included (oscillatory instability, gradient collapse, temporal accumulation failure).

Section 9.13: Stage 3B statistical results — anatomical gradient validation, 1000 permutation null, p_adjusted < 0.0001 for three anatomical classes. Ring interneuron suppression at class level confirmed.

Section 9.14: Stage 4 results (Run 401, Valid) — topology-dependent bifurcation confirmed at 127,536-node scale. Real graph sustains gradient 0.076 for all 100 steps. Random graph collapses to zero at step 23. Effect not explained by degree, weight, or type distribution. Signed input mean: real -7.23, random ~0. No oscillatory instability. No directional mean bias (distinct from Stage 3 geometry effect). Mechanistic statement: real connectome encodes non-uniform E/I input structure that prevents local cancellation; rewiring destroys this structure.

Section 11: Network Neuroscience Theory citation (Wilcox et al., 2026 Nature Communications) with modal control and weak tie connections to COS gradient mechanism.

Section 12: Three new limitations added — gradient magnitude scaling to human-scale HM, long-duration stability under high-variance input, E/I stability under signed constraint at scale.

Section 14: Stage 3B and Stage 4 conclusion paragraphs added.

References: Wilcox et al. (2026), Gu et al. (2015), Eckstein et al. (2024) added.