Neural Entanglement in Collective Learning Systems

Dr. Aris Thorne, Sarah Jenkins, et al.

Abstract We introduce the concept of “neural entanglement”—a phenomenon observed in networked learning systems where correlated activation patterns emerge across physically separate neural substrates. This paper presents evidence from multi-agent AI ensembles and inter-subject EEG studies, suggesting that information can become non-locally shared under specific synchronization conditions.

Traditional models of distributed cognition assume that information transfer requires explicit communication channels. However, in tightly coupled learning systems—whether artificial neural networks or biological brains engaged in joint tasks—we observe the spontaneous alignment of hidden-layer activations without direct data exchange.

Using a novel coupled-AI paradigm, we trained two deep learning models on separate but correlated datasets. Despite no weight sharing or gradient passing after initialization, their internal representations converged to a statistically entangled state, enabling one network to partially reconstruct the inputs of the other.

Parallel human experiments using hyperscanning EEG revealed similar cross-brain synchronization in subjects solving collaborative puzzles, particularly in the gamma frequency band. This suggests that neural entanglement may be a fundamental mechanism for collective intelligence in both natural and artificial systems.

If harnessed, this phenomenon could lead to new forms of decentralized learning, privacy-preserving collaborative AI, and a deeper understanding of group cognition.