Our world is full of networks. Our social networks are abstract, but others, such as the circuitry in our brains or the underground networks of fungi that trees use to communicate, have to exist physically. All brain regions are interlinked, forming one complex integrative system which is crucial for healthy cognitive functioning. Exactly how functional networks emerge and how robust they are is still unknown.
Physical networks, which are restricted by the laws of three-dimensional space, captured the interest of Nima Dehmamy, a postdoctoral researcher at Northeastern University’s Center for Complex Network Research.
Dehmamy and Soodabeh Milanlouei, a doctoral student in industrial engineering, built a physics-based computer model to find the most efficient layout for those networks.
“If you’re a neuron in the brain, every connection that you get occupies some space. The more connections you have, the more space you will require,” says Dehmamy.
A simple network tends to resemble an old ball-and-stick molecule set, with large nodes linked by small, relatively straight connections. There is plenty of space for connections to pass by each other without interfering.
But as the connections increase in both size and number, they start to get in each other’s way. The links have to bend and push against each other until the whole network begins to look more like a pile of spaghetti. The researchers found that this change begins to affect the way the network responds to physical stress. Simpler networks respond as an irregular solid might: Stress spreads unevenly. As a network becomes more complicated, however, it behaves increasingly as a gel would.
Understanding the physics of these theoretical networks can help researchers to study real-world networks such as the human brain. The research also lends itself to designing 3D-printable models of different networks. Printed models provide researchers a new way to study the layout and connections of a network.
Ishwarya Varshitha
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