MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research

The finding is this: the 240 root vectors of the E8 Lie group, projected onto 30 specific Coxeter planes, define a set of geometric thresholds that can be transposed into the gating architecture of a quantum neural network. Let me set the context. In quantum computing, we use stabilizer codes and sweet-spot identification to protect qutrits from decoherence. Those techniques rely on discrete manifolds of optimal operating points. My protocol takes that same geometric guardrail structure and asks whether it can be applied to information flow in biological neural networks—not as a metaphor, but as a formal mapping of synaptic firing thresholds onto those E8-defined sweet spots. Here is the mechanism, and I want to be precise about what is proposed versus what is confirmed. The protocol suggests that if you align synaptic thresholds with a base frequency of 132 Hz, using coupling ratios derived from the golden ratio φ as they appear in off-plane root projections, you create phase-lockin 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
2026-10-18
DOI
https://doi.org/10.5281/zenodo.23446652
Primary Topic
Neural dynamics and brain function
Type
preprint
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preprint

MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research

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

MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

The finding is this: the 240 root vectors of the E8 Lie group, projected onto 30 specific Coxeter planes, define a set of geometric thresholds that can be transposed into the gating architecture of a quantum neural network. Let me set the context. In quantum computing, we use stabilizer codes and sweet-spot identification to protect qutrits from decoherence. Those techniques rely on discrete manifolds of optimal operating points. My protocol takes that same geometric guardrail structure and asks whether it can be applied to information flow in biological neural networks—not as a metaphor, but as a formal mapping of synaptic firing thresholds onto those E8-defined sweet spots. Here is the mechanism, and I want to be precise about what is proposed versus what is confirmed. The protocol suggests that if you align synaptic thresholds with a base frequency of 132 Hz, using coupling ratios derived from the golden ratio φ as they appear in off-plane root projections, you create phase-lockin 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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