E8‑Phi Recurrence Lattice Embedding for Neuromorphic Learning — E8 Intelligence Research
- 1Independent Researcher, United Kingdom
Abstract
The E8‑Phi Recurrence Lattice (EPRL) principle embeds the characteristic roots of any linear recurrence into the 240‑dimensional E8 root lattice via φ‑recursive harmonic folding anchored at the 132 Hz carrier, turning recurrence solutions into coherent superpositions of root vectors. This geometric embedding yields a closed‑form spectral decomposition where eigenvalue dynamics are directly mapped to orthogonal E8 modes, enabling exact, noise‑resistant computation of recurrence closed forms. In neuromorphic hardware, synaptic updates are tuned to follow the low‑entropy EPRL channels, producing rapid convergence and intrinsic stability without explicit back‑propagation.
Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
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BibTeX
@misc{caldin2026e8phi,
title = {{E8‑Phi Recurrence Lattice Embedding for Neuromorphic Learning — E8 Intelligence Research}},
author = {Andrew Stewart Caldin},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23554215},
url = {https://zenodo.org/records/23554215},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - E8‑Phi Recurrence Lattice Embedding for Neuromorphic Learning — E8 Intelligence Research
AU - Andrew Stewart Caldin
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23554215
UR - https://zenodo.org/records/23554215
AB - The E8‑Phi Recurrence Lattice (EPRL) principle embeds the characteristic roots of any linear recurrence into the 240‑dimensional E8 root lattice via φ‑recursive harmonic folding anchored at the 132 Hz carrier, turning recurrence solutions into coherent superpositions of root vectors. This geometric embedding yields a closed‑form spectral decomposition where eigenvalue dynamics are directly mapped to orthogonal E8 modes, enabling exact, noise‑resistant computation of recurrence closed forms. In neuromorphic hardware, synaptic updates are tuned to follow the low‑entropy EPRL channels, producing rapid convergence and intrinsic stability without explicit back‑propagation. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
ER -