Yonatan Sanz Perl

You can better model brain data if you assume quantum-like entanglement. New work from our centre indicates that the brain expresses the efficiency of quantum computation through classical mechanisms. The brain is a magnificent specimen because it operates on 20w—or a banana and some water—and yet generates a coherent, stable, adaptive, and conscious inner universe that can build rockets, computers, fall in love, and construct empires and religions. And it does so against the backdrop of slow, wet, porous, and inexpensive bioelectric activity. Compare this to contemporary AIs, which are energy guzzlers and require massive data centres. The difference is likely 10,000x or more. Instead of looking interstellar for data centres, we should really be looking to the brain. First, you model the brain as a network of coupled oscillators (commonly used for whole-brain models). If you wire these coupled oscillators up like the brain’s connectome you get very interesting, very surprising, brain-like dynamics; such as criticality, metastability (via turbulence), etc. These stochastic dynamics are crucial for rapid information sharing and maintaining local and global integration. And when these dynamics are included in the model, it fits the brain like a glove. Interestingly, when you then include long-range exceptions to the exponential distance rule (common in mammalian brains), you get a spectral gap that separates the dominant modes from the noisy bulk. These dominant modes behave like coherent state-vectors and their interactions produce interference effects, i.e., quantum-like entanglement. These interference effects may be one of the secrets to how the brain rapidly binds distributed information into unified, context-sensitive states. The paper also demonstrates that QL entanglement provides the brain a richer dynamical repertoire at lower energetic cost. Keep in mind that this “quantum-like” entanglement arises from the interference of coupled oscillators, but the functional end state is analogous in that you get the same mathematical advantages. It’s super exciting and we have a lot more to share in coming months.