Does String Theory Solve the Mystery of the Brain?
Does string theory – the controversial “theory of everything” from physics – teach us anything about consciousness and the human brain?
Aside from the theory itself being designed by conscious humans using their brains, there is little reason to think so. In a nutshell, string theory is a broad field of theoretical physics that assumes that tiny vibrating strings provide the fundamental basis of reality. If valid, it offers ways to unify the quantum mechanics that governs the universe on a small scale with the gravitational force that shapes the cosmos on a larger scale. But the proposed strings are so unimaginably tiny, and their associated mathematics so difficult and diverse, that the theory is widely considered untestable experimentally. Consciousness, meanwhile, is a notoriously slippery and ill-defined thing, but it generally seems to be an emergent property of biology, like the assemblies of neurons in our brains.
There is no significant overlap between these very disparate fields. Or is it? A new article, published last week in Nature, posits that some of the obscure mathematics of string theory actually helps explain the wiring of a brain’s neurons, as well as the branching of other “physical networks” such as tree branches, blood vessels, and anthills. “This work,” trumpets an institutional press release, “represents the first time that string theory…has succeeded in describing real biological structures.”
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Lead author Albert-László Barabási, a distinguished professor and network scientist at Northeastern University, emphasizes that the paper does not claim any deep, direct relationship between string theory and neuroscience. Rather, it is about showing how the mathematical techniques developed in string theory can be used to better describe the way in which physical networks are organized. But even so, using the mathematics of string theory to understand neural wiring would be a surprisingly practical feat, given that the theory is so closely tied to physical reality that skeptical physicists call it “not even wrong.”
The potential link, says Barabási, comes from the fact that “physical networks are physically expensive and therefore try to optimize themselves,” although we don’t yet know exactly what they are optimizing for. The simplest approach would be a “wiring diagram” following the shortest routes between two nodes to minimize length, but detailed three-dimensional analyzes and physical network maps have revealed more complex branching geometries and connections that show a different optimization needs to occur. Barabási and his team therefore sought to explain how the structure of physical networks optimizes based on a minimum surface area rather than other factors such as length or volume.
“For many of these networks, like the vasculature that carries blood or the neurons that use ion channels to pump neurotransmitters, it’s actually a tube, and the biggest cost is building the surface,” he explains. “But modeling surface minimization is a hell of a math problem because you have to create locally smooth surfaces that connect to each other continuously.”
Barabási’s former postdoctoral fellow and first author of the study, Xiangyi Meng, now an assistant professor at Rensselaer Polytechnic Institute, realized that the seemingly intractable calculation was essentially the same as that for which string theorists had already developed sophisticated tools.
“Although the mathematics of minimal surfaces has deep historical roots, our work relies on a specific advance that classical geometry does not offer,” says Meng, namely a subtype of string theory called “covariant closed string field theory,” developed by Massachusetts Institute of Technology physicist Barton Zwiebach and others in the 1980s.
Covariant closed string field theory allows physicists to calculate the smoothest and most efficient, minimal surface-like interactions between certain types of strings by treating them as vertices (corners) and edges; This approach is important for attempts based on string theory to unify gravity and quantum mechanics. In the case of physical networks, Meng says, this offers a way to represent their growth as a series of sleeve-like surfaces that are sewn together smoothly. “Classical minimization tends to reduce sleeve-shaped surfaces to trivial wires,” he says. “Zwiebach’s formulation avoids this, by maintaining a finite thickness for each link. This fundamental idea is what allows us to model the three-dimensional reality of physical networks, such as neurons or veins, which must maintain their volume to function.”
The team then tested their approach on high-resolution 3D analyzes of physical networks, including those of neurons, blood vessels, tree branches and corals. In each case, they found that the string theory model produced a closer match than simpler classical predictions. In particular, the team’s model more accurately reproduced the observed branch numbers and alignments. “What we observed is a behavior that is not specific to the brain but universal across physical networks,” says Barabási. “This is a very important step, I think, in understanding the mechanisms by which brains and other physical networks connect and why they are unusual.”
“This article clearly shows that if you think [of physical networks] In terms of area costs rather than wire length, things start to make more sense,” says Michael Winding, a systems neuroscientist at the Francis Crick Institute in England, who was not involved in the work. “It’s really interesting. People generally think of surface area in terms of its effect on electrical properties, such as the speed at which signals travel through a neuron, rather than in terms of construction cost. build a neuron.
As to whether understanding the wiring of the brain actually requires techniques at the frontiers of theoretical physics, questions remain. True experts in both fields are rare. But Vijay Balasubramanian, a string theorist and brain biophysicist at the University of Pennsylvania, is skeptical.
“I’m not sure that this study marks a breakthrough in our understanding of physical networks, and many experts might find the claimed relationship with string theory unconvincing,” he says. “Any claims of revolutionary significance here therefore seem premature. That said, this effort to apply physical principles to the understanding of biological networks is a welcome addition to biophysics and neuroscience research and will hopefully inspire further research.”
