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  "slug": "hamiltonian-dynamics-as-the-engine-of-biological-computation",
  "title": "Hamiltonian Dynamics as the Engine of Biological Computation",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "This study investigates the physical basis of biological computation, focusing on the tension between static structuralism and dynamic evolution. Utilizing a Hamiltonian formulation of the Kuramoto model, we simulate a biological reservoir to rigorously evaluate the relationship between “Scale Inseparability” (coupling strength), Gamma oscillations (30-50 Hz), and computational utility. Our results demonstrate that biological Hamiltonian dynamics achieve superior dynamic stability and a 3.1x thermodynamic efficiency advantage over discrete digital baselines. However, we identify a critical “Readout Gap”: while the system forms stable, energy-minimized synchronization manifolds capable of protecting information, standard linear regression fails to decode this phase-encoded memory (MC ≈ 0). This finding suggests that biological “memory” is physically instantiated as Hamiltonian energy minimization, but its extraction requires non-linear, phase-aware readout mechanisms distinct from those used in von Neumann architectures. [...]",
  "abstract_markdown": "This study investigates the physical basis of biological computation, focusing on the tension between static structuralism and dynamic evolution. Utilizing a Hamiltonian formulation of the Kuramoto model, we simulate a biological reservoir to rigorously evaluate the relationship between “Scale Inseparability” (coupling strength), Gamma oscillations (30-50 Hz), and computational utility. Our results demonstrate that biological Hamiltonian dynamics achieve **superior dynamic stability** and a **3.1x thermodynamic efficiency advantage** over discrete digital baselines. However, we identify a critical “Readout Gap”: while the system forms stable, energy-minimized synchronization manifolds capable of protecting information, standard linear regression fails to decode this phase-encoded memory (\\(MC \\approx 0\\)). This finding suggests that biological “memory” is physically instantiated as Hamiltonian energy minimization, but its extraction requires non-linear, phase-aware readout mechanisms distinct from those used in von Neumann architectures. [...]",
  "date": "2026-01-01",
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        "description": "This study investigates the physical basis of biological computation, focusing on the tension between static structuralism and dynamic evolution. Utilizing a Hamiltonian formulation of the Kuramoto model, we simulate a biological reservoir to rigorously evaluate the relationship between “Scale Inseparability” (coupling strength), Gamma oscillations (30-50 Hz), and computational utility. Our results demonstrate that biological Hamiltonian dynamics achieve superior dynamic stability and a 3.1x thermodynamic efficiency advantage over discrete digital baselines. However, we identify a critical “Readout Gap”: while the system forms stable, energy-minimized synchronization manifolds capable of protecting information, standard linear regression fails to decode this phase-encoded memory (MC ≈ 0). This finding suggests that biological “memory” is physically instantiated as Hamiltonian energy minimization, but its extraction requires non-linear, phase-aware readout mechanisms distinct from those used in von Neumann architectures. [...]",
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