Despite consuming the equivalent of just two bananas per day, this doesn’t stop it from executing unconscionably complex tasks with impressive efficiency. But a team of researchers has designed a way to build a prototype of an artificial neuron made of unbelievably thin graphene slits housing a single layer of water molecules, according to a new study published in the journal Science.
And, instead of electrons, this artificial neuron uses ions.
Just like your brain.
A new artificial neuron uses ions, like the human brain
The brain’s ultra-high efficiency is contingent upon a base unit we know and love as the neuron, which consists of a neuron with nanometric pores called ion channels. These channels alternatively close and open depending on the stimuli, but the ion flows resulting from this process generate an electric current, one that emits action potentials, which are the crucial signals that let neurons communicate betwixt one another. Artificial intelligence (AI) can do it, too. But AI takes a lot more energy — tens of thousands of times more, to be precise. This is why the modern challenge for researchers in the field is to design and build electronic systems that rival the energy efficiency of the human brain.
The study of nanofluidics is relevant because it studies how fluids behave in channels of less than 100 nanometers wide. The new study showed how an electric field could assemble the single layer of water molecules into elongated clusters, which develop a key property called the memristor effect: When clusters retain a portion of the stimuli they’ve received in the recent past. Much like the human brain, the researcher’s design saw graphene slits reproduce the ion channels, in addition to ion flows, and clusters. Additionally, with the help of digital and theoretical tools, scientists discovered a way to assemble these special clusters so they’d reproduce the physical mechanism of emitting action potentials.
Artificial neurons might enhance brain-computer interface research, for better and worse
In other words, the ability to transmit information from one artificial neuron to another. This landmark work is ongoing within a French team, working with scientists at the University of Manchester, in the U.K. The next step is to experimentally prove these novel systems can execute basic learning algorithms that, in turn, may become a foundation for providing electric memory recall via artificial neurons. Naturally, the pursuit of replicating the activity of the human brain also has potential importance for augmenting neural functions. For example, Elon Musk’s Neuralink, which ultimately aims to implant computer chips in the brain to and kick off a new age of “super-human cognition,” when the intangible computing power of machine-learning analytics is combined with the (as yet) unparalleled creative intuition of the human mind.
However, there are drawbacks, not all of them only potential ones. “Without proper regulations [of brain chips], your innermost thoughts and biometric data could be sold to the highest bidder,” wrote the philosopher and cognitive psychologist Susan Schneider in an Observer report. “People may feel compelled to use brain chips to stay employed in a future in which AI outmodes us in the workplace.” And, if brain chips like Musk’s Neuralink enable or enhance the familiar activities of smartphones, this data might be scanned for signs of possible criminal digital activity with tools that are easily fooled into making a false positive, and reporting you for highly stigmatizing crimes. This might sound like a stretch, but a recent announcement from Apple reveals that the line between privacy and public security is rapidly diminishing.