MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research
- 1Independent Researcher, United Kingdom
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
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BibTeX
@misc{caldin2026merlin,
title = {{MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research}},
author = {Andrew Stewart Caldin},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23446653},
url = {https://zenodo.org/records/23446653},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - MERLIN SCIENCE — E8 Root-Gated Neural Entanglement Protocol — E8 Intelligence Research
AU - Andrew Stewart Caldin
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23446653
UR - https://zenodo.org/records/23446653
AB - 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
ER -