Neuroscientists of University College London (UCL) have found that there is a simple pattern modeling the tree-like shape of brain’s neurons. They have shown how a simple computer program connecting points with links as little as possible can produce tree-like shapes very similar to the ones of real neurons. These shapes follow a power law, which is a mathematical law quite common across the natural world, and underlying complex structures. This is the law:

L = (3/4π)^1/3 × V^1/3n^2/3

where n is the number of dendritic sections to make up the tree, L is the total length of these sections, and V is the total volume

Neuron shape model: target points (red) distributed in a spherical volume and connected to optimize wiring in a tree (black) (credit: H. Cuntz et al./PNAS)

Similar theories about neurons networks have been already published in the past. This time, UCL Neuroscientists tested this theory by examining neurons in the olfactory bulb, a part of the brain where new brain cells are constantly being formed, and found that the growth of these neurons indeed also follows the power law, providing further evidence to support the theory. “The ultimate goal is to understand how the impenetrable neural jungle can give rise to the complexity of behavior” said the UCL Neuroscientists.

Why these results might be very interesting for us ?

Many communication and social networks have power-law link distributions, containing a few nodes that have a very high degree and many with low degree. The high connectivity nodes play the important role of hubs in communication and networking, a fact that can be exploited when designing efficient search algorithms. It has been shown that the Internet backbone and web page hyperlinks have a power-law distributions. And the same distributions might be applicable to future edge networks.

In fact, imagine edge networks evolving towards ensembles of huge numbers of interacting lightweight nodes capable of abstracting communications, processing and storage resources. Millions of nodes, like neurons, embedding simple “hard-wired rules”, will be capable of interacting, self-adapting and self-adjusting to cope with dynamic contexts (e.g. Users’ requests and business goals).

This is very much similar to what’s happening in the brain networks…