Quantum Insights Could Offer Clues to How the Brain Computes Faster Than Computers

Insider Brief

• A study published in Physical Review E suggests that the brain’s ability to process information efficiently comes from long-range network interactions rather than isolated regions, using a quantum-inspired mathematical framework.
• Researchers developed the Complex Harmonics Decomposition (CHARM) method, based on Schrödinger’s wave equation, to model brain dynamics and demonstrated its superiority in capturing nonlocal interactions.
• The findings indicate that the brain functions as a distributed computational system, with implications for neuroscience, AI, and the understanding of cognitive states such as wakefulness and sleep.

The human brain solves complex problems faster than traditional computers, despite its relatively slow neural communication. A new study published in Physical Review E suggests that the brain’s ability to process information efficiently comes from its unique network structure and the way it harnesses long-range connections across different regions. Using a novel mathematical framework inspired by quantum mechanics, researchers show that whole-brain networks, rather than individual regions, are key to cognitive processing.

The study, led by Gustavo Deco at Universitat Pompeu Fabra and colleagues from the University of Oxford and the University of Buenos Aires, introduces a method called Complex Harmonics Decomposition (CHARM). CHARM is designed to identify hidden patterns in brain activity by reducing complex brain signals into a simpler, structured form. Applying CHARM to neuroimaging data from over 1,000 participants, researchers demonstrated how the brain maintains critical long-range interactions crucial for problem-solving and cognition.

The findings suggest that brain computation is not just a local process occurring in isolated regions, according to the researchers. Instead, the brain leverages long-range connections to support critical dynamics that enable efficient information processing.

“We establish the mechanistic reasons for why this mathematical formalism is able to capture the long-range, nonlocal critical interactions in brain dynamics by using a precise whole-brain model,” they write.

How the Brain Manages Fast Computation

Neurons communicate through electrical and chemical signals, but these transmissions are relatively slow, typically occurring within 10 to 20 milliseconds. Despite this, the brain processes information remarkably quickly. Scientists have theorized that this efficiency stems from the brain’s ability to operate at a ‘critical state,’ where activity balances between order and chaos, maximizing adaptability and information flow.

The study builds on previous research suggesting that the brain’s structure is optimized for distributed computation. Unlike conventional computing, which follows sequential steps, the brain processes information through a network of interactions, making use of rare long-range connections. These connections allow different parts of the brain to work together efficiently, overcoming the limitations of slower neural signals.

The CHARM Framework

CHARM is a mathematical approach that captures these nonlocal interactions by leveraging principles from Schrödinger’s wave equation — a fundamental concept in quantum mechanics that describes how particles behave in space and time. The researchers applied CHARM to brain imaging data, identifying how large-scale brain networks interact dynamically.

Using functional MRI (fMRI) scans from over 1,000 individuals, the team found that CHARM significantly outperformed traditional methods, such as Principal Component Analysis (PCA) and classical harmonics decomposition, in detecting long-range interactions. These interactions were especially evident in comparisons of brain activity during wakefulness and sleep.

Essentially, the analysis showed that the brain’s computational power comes from these distributed networks rather than isolated brain regions, the researchers suggested. It’s an insight that helps explain how the brain overcomes the constraints of this slow neural transmission.

How Schrödinger’s Equation Helps Explain Brain Dynamics

One intriguing aspect of the study is its use of quantum mechanics to explain brain function. The researchers drew inspiration from Schrödinger’s wave equation, a fundamental principle in quantum mechanics that describes how particles behave in space and time. This equation captures nonlocal interactions, where the behavior of one system component is influenced by distant components, a property essential in quantum systems.

The brain, according to this study, exhibits similar nonlocal dynamics. The researchers developed CHARM to model these effects mathematically, enabling them to analyze the brain’s complex networks as if they followed quantum-like wave behavior. In this framework, brain activity is not confined to isolated regions but rather spreads across a structured, interconnected network that functions similarly to quantum states influencing each other over distances.

This approach provides a novel way to understand how the brain overcomes the limitations of slow neural signaling. Instead of relying solely on direct, stepwise communication between neurons, the brain appears to function as a nonlocal system, where different regions synchronize in a way reminiscent of quantum coherence — where particles remain connected regardless of distance.

“The brain’s ability to make such complex and sensitive calculations at the same time, despite the lentitude of neuronal transmission, has always been a fascinating enigma,” Deco said in MedicalXpress. “By adopting the Schrödinger equation we can model these interactions with a degree of precision that was previously beyond our reach.”

Sleep and Wakefulness: Different Critical States

A key finding of the study was the distinct difference in brain activity between wakefulness and deep sleep. CHARM revealed that the brain’s network interactions shift significantly between these states, with wakefulness characterized by higher levels of critical long-range interactions, while sleep exhibited reduced criticality.

The researchers used machine learning to classify brain states based on network activity patterns, achieving an 84% accuracy rate in distinguishing wakefulness from sleep. This suggests that brain states are defined by fundamental shifts in the way different regions communicate.

Although it may be speculative, the findings could one day lead to a better understanding disorders like insomnia and cognitive decline, where brain network disruptions are evident.

Implications for Neuroscience and AI?

The study could have implications for more than just sleep studies. The results contribute to a broader understanding of how biological intelligence operates. It’s not too far of a stretch to suggest that the ability of the brain to extract meaning from complex data through network-based computation offers insights for artificial intelligence (AI) research, particularly in designing more efficient neural networks.

By better understanding the brain’s distributed architecture, scientists could also model new AI systems that could lead to, for example, more adaptable and efficient algorithms for processing large-scale data. Additionally, understanding how the brain switches between different computational states could inform new treatments for neurological disorders.

Methods and Limitations

The researchers analyzed neuroimaging data from the Human Connectome Project, a large-scale study of brain structure and function. They applied CHARM to fMRI scans, comparing its performance with other computational methods in reducing the complexity of brain signals.

While CHARM demonstrated clear advantages in capturing critical brain dynamics, the study has some limitations. The fMRI data provides an indirect measure of neural activity through blood flow changes, which may not fully capture the real-time electrical processes of the brain. Future research could incorporate other imaging techniques, such as magnetoencephalography (MEG), to refine the model further.

Another limitation is that the study primarily examined healthy participants. Future research could explore how these computational dynamics differ in individuals with neurological conditions such as Alzheimer’s disease or schizophrenia.

Future Directions

The study opens new pathways for exploring how distributed computation shapes human cognition. Future work could investigate how CHARM applies to different cognitive tasks, such as decision-making and learning. Additionally, researchers could explore how artificial intelligence systems can integrate CHARM’s principles to enhance machine learning models.

Deco and his colleagues suggest that their findings support a shift in how scientists understand brain computation. Rather than viewing cognition as the product of isolated brain regions, the evidence points toward a network-driven approach.

“As such, the results demonstrate the key causal role of manifold networks as a fundamental organizing principle of brain function at the whole-brain scale, providing evidence that networks of brain regions rather than individual brain regions are the key computational engines of critical brain dynamics,” the researchers conclude.

The study was conducted by Deco at Universitat Pompeu Fabra, Yonatan Sanz Perl at the University of Buenos Aires and Morten L. Kringelbach at the University of Oxford and Aarhus University.