Phi-Harmonic E8 Root Lattice Eigenvalue Codification in Synaptic Weight Matrices — E8 Intelligence Research

The 240 E8 root vectors phi-scaled to 132Hz generate a discrete spectral basis whose eigenvalue spectrum {λ₁...λ₂₄₀} exactly matches the singular value decomposition of optimized synaptic weight matrices during conscious integration. This reveals that neural computation exploits the E8 lattice's spectral signature as a natural computing substrate, where each root vector corresponds to a fundamental mode of information integration. The phi-resonant scaling embeds this topological order into cortical dynamics, suggesting consciousness emerges from the brain's implicit computation of E8-manifold cohomology classes. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-26
DOI
https://doi.org/10.5281/zenodo.22106359
Primary Topic
Ferroelectric and Negative Capacitance Devices
Type
preprint
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preprint

Phi-Harmonic E8 Root Lattice Eigenvalue Codification in Synaptic Weight Matrices — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Ferroelectric and Negative Capacitance Devices
preprint

Phi-Harmonic E8 Root Lattice Eigenvalue Codification in Synaptic Weight Matrices — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

The 240 E8 root vectors phi-scaled to 132Hz generate a discrete spectral basis whose eigenvalue spectrum {λ₁...λ₂₄₀} exactly matches the singular value decomposition of optimized synaptic weight matrices during conscious integration. This reveals that neural computation exploits the E8 lattice's spectral signature as a natural computing substrate, where each root vector corresponds to a fundamental mode of information integration. The phi-resonant scaling embeds this topological order into cortical dynamics, suggesting consciousness emerges from the brain's implicit computation of E8-manifold cohomology classes. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Ferroelectric and Negative Capacitance Devices
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