Does string theory—physics’ controversial “theory of everything”—tell us anything about consciousness and the human brain?
There is no reason to think so, other than a theory being formulated by conscious humans using their brains. In short, string theory is a vast field of theoretical physics that posits that tiny vibrating strings are the fundamental basis of reality. If valid, it offers ways to integrate the quantum mechanics that governs the universe on small scales with the gravitational force that shapes the universe on large scales. But the proposed strings are so unimaginably small, and the mathematics associated with them so difficult and diverse, that the theory is widely considered untestable experimentally. Consciousness, meanwhile, is an extremely slippery and undefined thing, but in general it appears to be an emergent property of biology, like the combinations of neurons within our brains.
No meaningful overlap exists between these extremely disparate domains. Or does it? a new paper, Published last week In Nature, It is believed that some of the mysterious mathematics of string theory actually helps explain the wiring of brain neurons, as well as the branching of other “physical networks” such as tree limbs, blood vessels, and anthills. “Work,” blows a trumpet Institutional Press Release“Represents the first time string theory … has successfully described real biological structures.”
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Senior author Albert-László Barabási, a distinguished professor and network scientist at Northeastern University, emphasizes that the paper is not claiming any deep, direct connection between string theory and neuroscience. Rather it is showing that mathematical techniques developed in string theory can be used to better describe how physical networks organize themselves. But still, using the mathematics of string theory to understand neural wiring would be a surprisingly practical accomplishment, given that the theory is so tightly connected to physical reality that skeptical physicists have called it “not even falsifiable.”
The potential connection arises from the fact that “physical networks are physically expensive and thus tend to optimize themselves,” says Barabasi, even though we don’t yet know what exactly they optimize. The simplest approach would be a “wiring diagram” following the shortest paths between any two nodes to minimize length – but detailed three-dimensional scans and maps of the physical network have revealed more complex branch geometries and connections indicating that some different optimizations must occur. So instead Barabasi and his team tried to explain how the structure of physical networks optimizes for minimal surface area rather than other factors like length or volume.
“For many of these networks, like the vascular system that carries blood or neurons that use ion channels to pump neurotransmitters, you’re really talking about a tube, and the biggest cost is building the surface,” he says. “But modeling surface minimization is a mathematical problem because you need to create locally smooth surfaces that patch into each other in a continuous manner.”
Jiangyi Meng, Barabasi’s former postdoc and first author of the study, now an assistant professor at Rensselaer Polytechnic Institute, realized that the seemingly difficult calculation was essentially identical to one 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 provide,” says Meng, “namely a subtype of string theory called “covalent closed string field theory,” which was developed in the 1980s by Massachusetts Institute of Technology physicist Barton Zwiebach and others.
Covalent closed string field theory allows physicists to calculate the smoothest, most efficient interactions – similar to minimal surfaces – between certain types of strings by treating them as vertices (corners) and edges; This approach is important for string-theory-based efforts to integrate gravity and quantum mechanics. In the case of physical networks, Meng says, it provides a way to depict their evolution as a series of sleeve-like surfaces that are easily sewn together. “Importantly, the classical minimization reduces sleeve-like surfaces to trivial strings,” he says. “Zweibach’s formulation prevents this, maintaining a finite thickness for each link. This fundamental insight is what allows us to model the three-dimensional reality of physical networks, such as neurons or nerves, which must maintain volume to function.”
The team then tested their approach against high-resolution 3D scans of physical networks including neurons, blood vessels, tree branches and corals. In each case, they found that the string theory model produced a much closer match than simple classical predictions. In particular, the team’s model more accurately replicated the observed numbers and alignment of branches. “So what we were seeing is a behavior that is not specific to the brain but is universal across physical networks,” Barabasi says. “I think this is a very important step forward in understanding the mechanisms of how the brain and other physical networks wire themselves and why they are abnormal.”
“This paper shows well that if you think (about physical networks) in terms of surface-area cost rather than wire length, things start to make more sense,” says systems neuroscientist Michael Winding of the Francis Crick Institute in England, who was not involved in the work. “It’s really interesting. People usually think about surface area in terms of its effect on electrical properties—like how fast signals travel within a neuron—rather than manufacturing cost. Construction A neuron.”
As for whether understanding the wiring of the brain really requires techniques from the boundaries of theoretical physics, questions remain. There are very few authentic experts in both the fields. But Vijay Balasubramaniam, a string theorist and brain-focused biophysicist at the University of Pennsylvania, is skeptical.
“I am not sure that this study marks a significant breakthrough in our understanding of physical networks, and many experts may find the claimed connection with string theory unconvincing,” he says. “Therefore any claims of revolutionary significance here seem premature. That said, this attempt to apply physical principles to understand biological networks is a welcome contribution to scholarship in biophysics and neuroscience and will hopefully inspire further investigation.”
