Summary
Lead: The human brain consumes only about 20 watts of power, yet it can perform highly complex cognitive tasks using slow, noisy biological components. How it balances flexibility, stability, and low energy consumption remains a key question in neuroscience. This article, published in
The human brain consumes only about 20 watts of power, yet it can perform highly complex cognitive tasks using slow and noisy biological components. How it balances flexibility, stability, and low energy consumption remains a key question in neuroscience. This study published in Advanced Science proposes that the brain may not need to maintain microscopic quantum states; instead, it can generate quantum‑like dynamics through classical neural oscillation networks. The research team used resting‑state brain imaging data from 1,003 participants to build a whole‑brain model, offering a new theoretical framework for understanding the brain’s efficient information processing.
Research fields: Quantum-Like Dynamics, Human Connectome, Whole-brain Modeling, Spectral Gap, Växjö Interference, Metastability
王璇 | Author
Paper title: Quantum-Like Dynamics in Whole-Brain Models of the Human Connectome
Paper link: https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.77103
Publication date: August 18, 2026
What is “quasi-quantum” brain dynamics?
“Classical quantum” does not imply that the human brain is a quantum computer, nor that microscopic quantum entanglement usable for computation has been found in neural tissue. The research team focuses on a coupled network composed of a large number of classical oscillators. When the oscillators in the network interact, a relatively stable collective synchronization state may emerge. If the dominant eigenvalue of the network’s connectivity matrix is clearly separated from the other eigenvalues, a large spectral gap appears. The more pronounced the spectral gap, the more easily the dominant synchronized state stands out from background noise and resists external perturbations.
Existing theoretical studies have shown that classical oscillatory networks with this spectral structure may produce quantum-like probabilistic effects. Based on this, researchers have proposed that the brain’s special connectivity structure might also support similar macroscopic dynamics. The human connectome contains a small number of long-range connections that span distant brain regions; these may link local synchronous states of different areas, helping the whole brain generate rich and flexible activity patterns. By attempting to combine coupled oscillators from physics, the human connectome from neuroscience, and computational modeling, we aim to answer a concrete question:If a whole‑brain model incorporates network structures that support quantum‑like dynamics, can the model better reproduce real human brain activity?
Figure 1 Framework for discovering quasi‑quantum (QL) dynamics in the human brain
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How can a whole‑brain model validate quasi‑quantum dynamics?
The research team used data from the Human Connectome Project (HCP) to analyze resting-state functional MRI of 1,003 participants. The investigators built two whole-brain models with similar overall structure: one model features local oscillatory networks with a large spectral gap, referred to as the quasi-quantum model. The other model has a smaller spectral gap, referred to as the non-quasi-quantum model. Subsequently, each brain region was coupled according to the actual human structural connectome.
Figure 2 Schematic of whole-brain modeling with and without class quantum effects.
The researchers compared the similarity between functional connectivity patterns generated by two models and actual resting‑state brain activity. The results showed that the quasi‑quantum model can reproduce empirical data more accurately, and its overall fitting error is markedly lower than that of the non‑quasi‑quantum model. This implies that network structures with larger spectral gaps and stable collective synchrony are, at least at the modeling level, better suited to describe large‑scale human brain dynamics. The research team also used the COCO framework, which combines stochastic thermodynamics with whole‑brain modeling, to estimate the thermodynamic cost required for the model to perform information processing and inter‑regional communication. The results indicated that the inferred energy cost of the quasi‑quantum model is lower than that of the non‑quasi‑quantum model.
To obtain evidence more direct than the spectral gap, Växjö interference analysis was further introduced. Classical probability theory holds that the total probability of an event can be obtained by weighted summation of probabilities under different conditions. If the actual probabilities systematically deviate from this additive relationship, a probability structure resembling quantum interference may emerge. The study found that as the model becomes increasingly close to real resting‑state brain activity, its Växjö interference also strengthens, and the interference magnitude is significantly correlated with the size of the spectral gap. This indicates that quasi‑quantum features at the structural level of the model and probabilistic interference at the functional level appear simultaneously.
Figure 3: Functional and structural quantum-like effects observed in the full brain dynamics model.
Rare long-range connections in the human brain also play an important role. When researchers removed connections longer than 80 mm from individualized whole-brain models, the spectral gap shrank markedly, and the link between the spectral gap and metastability weakened. Metastability describes the brain’s ability to continuously switch among multiple temporarily stable synchronized states and is thought to be closely related to cognitive flexibility. Thus, rare long-range connections may serve as information channels across brain regions, amplify quasi‑quantum dynamics, and help the brain maintain richer activity states.
What does this study mean?
This work proposes and preliminarily tests a new framework for describing large-scale brain activity: a human brain network composed of classical oscillators that may exhibit quantum-like probabilistic rules due to specific connectivity structures. The quantum-like whole-brain model fits resting-state brain imaging data better than a matched non-quantum-like model and shows lower model-derived energy cost. However, it has not demonstrated that the real brain performs quantum-like computation, nor has it found microscopic quantum processes. Spectral gaps themselves are also widespread in classical networks; the current results mainly come from correlation modeling and further validation is needed using control models that match spectral gaps, neuronal timescale data, and pharmacological or brain stimulation experiments. Nevertheless, this study turns an imaginative question into a measurable, comparable, and falsifiable scientific proposition, and provides a new direction for understanding how the human brain achieves flexible and efficient information processing in a noisy environment.
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