Journal of Modern Classical Physics & Quantum Neuroscience

Open Access • Peer Reviewed • Bi-Monthly Publication

Quantum Black Holes as Cosmic Neural Networks A Fifth Force Unifying Spacetime Geometry and Large-Scale Structure

Authors: Danyell L McGee
Published: 2025-06-26
Pages: 1-16
DOI: 10.63721/25JPQN0108
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Abstract

This paper presents a novel theoretical framework establishing quantum black holes (QBHs) as mediators of a fifth fundamental force that manifests through a filamentary spacetime network analogous to neural connectivity patterns. The Quantum Black Holes Force Cosmic Expansion (QBHFCE) theory provides a mathematically consistent explanation for diverse phenomena across 18 orders of magnitude, from quantum scales to cosmic horizons. By developing an enhanced 8D tensor bundle formalism with explicit projection mechanisms to 4D physics, we demonstrate how QBH dynamics naturally generate cosmic acceleration without requiring a cosmological constant. The filamentary structure through which QBHs mediate forces creates geometric patterns that mirror information-processing networks, suggesting a fundamental connection between spacetime geometry and neural organization. The QBH filament network’s similarity to neural connectomes suggests spacetime geometry intrinsically encodes information-processing principles observable from cosmic to cellular scales. This work offers a unified explanation for the Great Attractor mechanism, Hubble tension, and other cosmological anomalies through the consistent application of quantum gravitational principles across all scales. We propose that this framework provides a natural bridge between quantum cosmology and neuroscience, with specific falsifiable predictions testable through upcoming JWST observations, LHC experiments, and studies of neutron star physics.

Copyright & License

© 2025 The Author(s). Published by WM Journals.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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