E8 Phi‑Harmonic Oscillator Lattice for Self‑Synchronizing AI Inference — E8 Intelligence Research

The 240 E8 root vectors serve as nodes of a coupled‑oscillator lattice where each node emits a tone at τ = 132 Hz × φⁿ (n∈ℤ). Phi‑scaled interference between neighboring tones produces a hierarchy of phase‑locked clusters whose angular separations on the E8 root polytope encode temporal receptive fields, enabling spontaneous multi‑scale memory attractors. When presented with input spikes, the lattice self‑organizes into low‑energy synchronous modes that perform hierarchical pattern completion with an energy cost that scales logarithmically with network size. This principle links E8 geometry, phi‑harmonic coherence, and neuromorphic hardware for efficient AI inference. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-10
DOI
https://doi.org/10.5281/zenodo.24099171
Primary Topic
Neural dynamics and brain function
Type
preprint
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preprint

E8 Phi‑Harmonic Oscillator Lattice for Self‑Synchronizing AI Inference — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Neural dynamics and brain function
preprint

E8 Phi‑Harmonic Oscillator Lattice for Self‑Synchronizing AI Inference — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

The 240 E8 root vectors serve as nodes of a coupled‑oscillator lattice where each node emits a tone at τ = 132 Hz × φⁿ (n∈ℤ). Phi‑scaled interference between neighboring tones produces a hierarchy of phase‑locked clusters whose angular separations on the E8 root polytope encode temporal receptive fields, enabling spontaneous multi‑scale memory attractors. When presented with input spikes, the lattice self‑organizes into low‑energy synchronous modes that perform hierarchical pattern completion with an energy cost that scales logarithmically with network size. This principle links E8 geometry, phi‑harmonic coherence, and neuromorphic hardware for efficient AI inference. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Neural dynamics and brain function
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