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Quantum Machine Learning Quantum Simulation Open Quantum Systems Decoherence Entanglement Theory Quantum Correlations

Coherence time in biological oscillator assemblies bounds the rate of state registration.

PubMed
Authors: Todd I

Year

2026

Paper ID

30468

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

217

Citations

0

Abstract

The physical principles determining how fast distributed neural assemblies can synchronize-and thus how fast the neural processes underlying cognition can proceed-remain incompletely formalized. We derive a coherence time bound for coupled oscillator networks: the minimum interval between irreversible state registrations ("commits") scales exponentially with coordination depth M. For M semi-independent modules requiring phase alignment at coherence r, the commit rate is dominated by rare-event first-passage time on the configuration torus, producing a fundamental speed-flexibility trade-off-increasing M expands combinatorial flexibility but slows commits exponentially; increasing r accelerates synchronization but restricts dynamics to lower-dimensional manifolds. Kuramoto simulations validate the expected scaling in modular networks R=0.97 and delineate regime boundaries in all-to-all and sparse topologies. Using independently constrained parameters, the framework recovers the empirical 30-50 ms visual binding window and shows that coordination time dominates quantum, thermodynamic, and power limits in biological neural systems by many orders of magnitude. We also develop exploratory extensions of the framework: a pre-commit "phase delta" regime in which structured phase offsets bias downstream outcomes before registration, candidate biophysical substrates for that regime, and qualitative predictions for alpha entrainment, stress-related temporal distortion, and pharmacological modulation of subjective time. The central claim is therefore modest and testable: in systems with identifiable modular coordination architecture, multi-module phase alignment can be the dominant rate-limiting step on biological processing.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • The physical principles determining how fast distributed neural assemblies can synchronize-and thus how fast the neural processes underlying cognition can proceed-remain...

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