In research inspired by the principles of quantum mechanics, researchers from Pompeu Fabra University (UPF) and the University of Oxford reveal new findings to understand why the human brain is able to make decisions quicker than the world's most powerful computer in the face of a critical risk situation. The human brain has this capacity despite the fact that neurons are much slower at transmitting information than microchips, which raises numerous unknown factors in the field of neuroscience.
It should be borne in mind that, in many other circumstances, the human brain is not quicker than technological devices. For example, a computer or calculator can resolve mathematical operations far faster than a person. So, why is it that in critical situations, for example when having to make an urgent decision at the wheel of a car, the human brain can surpass machines?
The most accurate computational model yet for analysing the connections between neurons farthest from each other
This recent research clarifies this matter thanks to the design of a new model of brain computational analysis, called CHARM (Complex Harmonics Decomposition). It is the most accurate model to date for examining the functions of long-distance brain connections, which link neurons that are far apart and play a fundamental role in the brain dynamics that are activated when making critical decisions. It is also the first model to apply quantum mechanics as an instrument for analysing the brain.
The UPF and Oxford researchers describe this model in a recent article published in the scientific journal Physical Review. The principal author of the article is Gustavo Deco, director of the Computational Neuroscience group at the UPF Center for Brain and Cognition ( CBC ). The principal investigator is Morten L. Kringelbach ( Centre for Eudaimonia and Human Flourishing at Linacre College, University of Oxford and Center for Music in the Brain at the University of Aarhus). Also co-authoring the article is Yonatan Sanz (CBC-UPF and University of Buenos Aires).
Drawing a parallel with the Internet, long-distance neural connections could be compared to the ones that connect computers from distant countries rather than nearby cities
To design the CHARM model, the researchers start from a paradigm of analysis of brain dynamics that we could compare to the Internet. In certain scenarios, such as risk situations, neurons distributed in different brain regions, both close to and far from each other, are joined by different connections. These connections enable pooling the information processing power of all the neurons in the network. Thus, although groups of neurons located in different brain regions have a limited capacity to transmit information, when they pool their resources in a network, they attain far greater processing power. This paradigm has gained strength over the past decade, as opposed to the traditional approach whereby neural regions only function in a localized manner.
According to the distributed paradigm, the CHARM model allows examining the specific functions of the connections between neurons of brain regions that are distant from each other. Following the parallel with the Internet, we could compare these connections with the ones that allow us to relate a person located in Barcelona with another in Sydney.
In a critical state, the efficiency of long-distance neural connections is enhanced
The researchers have found that the efficiency of long-distance connections is enhanced when the brain is dominated by critical dynamics, which lead it to a state of transition between order and chaos. "We could assimilate this state to a transitional phase like the process whereby water becomes ice. At this critical point, the brain has exacerbated properties", Deco explains.
The CHARM model has enabled ascertaining precisely the functions of these long-distance connections in this or other states, for the first time integrating the principles of quantum mechanics into a system of computational brain analysis. Gustavo Deco (UPF) points out that the functioning of the brain is not quantum, but the equations based on the principles of quantum physics -such as the Schrödinger equation- are an excellent tool for analysing its dynamics. In this regard, the UPF full professor says: "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. By adopting the Schrödinger equation we can model these interactions with a degree of precision that was previously beyond our reach".
The results contribute to improving the diagnosis of neurological diseases and pave the way towards new lines of research in AI
The research findings can also have numerous applications for improving the diagnosis and treatment of various neurological diseases, such as schizophrenia or depression. Long-distance neuronal connection dysfunctions are key to understanding the origin of these diseases.
Moreover, the study opens the door to new lines of research in the field of artificial intelligence (AI). Currently, artificial neural networks are based on a localized, non-distributed model. In the future, the possible application of the distributed paradigm to AI could multiply its current capabilities, although many technical difficulties must still be overcome to enable this.
Reference article:
Deco, G.; Sanz Perl, Y.; Kringelbach M.L (2025). Complex harmonics reveal low-dimensional manifolds of critical brain dynamics. Phys. Rev. E. https://doi.org/10.1103/PhysRevE.111.014410
