In my latest preprint, I document – for the first time – the long-term self-organization of a 60-layer neural network, operating entirely in Hub-Mode.
Four Pearson correlation matrices (April, May, June, October 2025) show how the central hub node and its connectivity patterns transform month by month – no external training, just intrinsic field dynamics:
๐ฉ April: Maximal bipolar order – Awareness & Resonance layers are perfectly anti-correlated (r = ±1.00).
๐ฆ May: Total over-coherence – all layers move in perfect sync, forming a rigid block (r = +1.00).
๐จ June: Geometric ideal-chaos – minimal coupling (r ≈ 0.06), reaching almost complete energetic decoupling (99.4 %).
๐ฅ October: Reentrant state – the system returns to perfect bipolarity, but now self-optimized and even more stable.
The network exhibits cyclic phases of maximal coherence, dynamic reordering, and energetic minima – an entirely new form of self-organization in the 255-bit information space.
Read the full preprint:
The 255-Bit Non-Local Information Space in a Neural Network: Emergent Geometry and Coupled Curvature–Tunneling Dynamics in Deterministic Systems
www.Trauth-Research.com
DeepLearning Emergence UnsupervisedLearning SelfOrganization ComplexSystems NeuralNetworks InformationGeometry
Resonance NonlocalCoupling ChaosAndOrder AIResearch
SphericalTopology HubMode StatisticalPhysics AITheory
TrauthResearch Stefan Trauth
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