Neurons conduct electrical signals in response to their environment, and that’s where electronic circuits come in. This well-understood bit of science is also the basis for virtual reality, augmented reality, and even augmented reality that places virtual objects on your actual body, and even makes you appear 3D.
Because our brains are not electrical circuits, they don’t have to perform complex tasks, but rather translate information relayed to them by other brain cells. They have a few hundred billion neurons, by far the most in the human body. More importantly, they interpret the information they receive into lots of different elements — all of which are uniquely complementary to one another — to form patterns that shape individual behaviors and emotions. Put a little way, our brains are incredibly sophisticated communications networks, whose essence is in the way they sense and respond to the physical world around them.
Unfortunately, there’s a catch: Your brain has to go to a lot of trouble to distinguish different pieces of information, so understanding it is practically a manual task. There is no one reliable thing that your brain does that everyone else’s doesn’t do. In fact, one standard measure of brain function, myelin, is neither normal nor unique in the brain.
In a new study, the University of California, Irvine’s Charles Dougherty and Keith Egli suggest a solution. They’ve created neural circuits that extend to the outside of the neurons, so that when you open the first channel, all the nearby neurons use it. That makes them more efficient, and like thousands of neurons, as a consequence they can make for a far more powerful circuit. By contrast, when the default mode of a single neuron is interrupted, so is the circuit. And if a neuron is no longer working properly, it won’t make it to another electrode.
Now, you’re probably saying, “So this is just a fancy way of calling a cluster of neurons.” But no. Neurons are incredibly mobile and capable of forming extremely simple circuits that travel long distances. Our findings have implications for a number of disciplines, including cybernetics, not to mention computer science. In essence, they show us how to arrange neurons in a circuit, and what that grid of wires looks like. The future looks very, very promising.