Honey might be a sweet solution for developing environmentally friendly components for neuromorphic computers, systems designed to mimic the neurons and synapses found in the human brain. Hailed by some as the future of computing, neuromorphic systems are much faster and use much less power than traditional computers. Washington State University engineers have demonstrated one way to make them more organic too. In a study published in Journal of Physics D, the researchers show that honey can be used to make a memristor, a component similar to a transistor that can not only process but also store data in memory. "This is a very small device with a simple structure, but it has very similar functionalities to a human neuron," said Feng Zhao, associate professor of WSU's School of Engineering and Computer Science and corresponding author on the study.

As the global chip shortage has been on the agenda for years, worries about it got even worse. On top of it, Russia's invasion of Ukraine has had a negative influence on the situation as well. Well, it seems there is. Engineers from Washington State University (WSU) have demonstrated that honey could be used to produce eco-friendly and brain-like computer chips, according to a study published in the Journal of Physics D. Inspired by human synapses, researchers processed honey into a solid form and jammed it between two metal electrodes to produce a structured design dubbed'memristor.' Honey memristors are able to mimic human neurons and switch on and off very quickly while maintaining information. "Honey does not spoil," said Feng Zhao, associate professor of WSU's School of Engineering and Computer Science and corresponding author of the study.

Scientists can use nanomaterials to mimic the human brain's structure. Despite its name, artificial intelligence isn't all that smart -- at least when compared to human brains. A.I.'s are excellent number crunchers and pattern finders, but when it comes to actual human-level cognition and problem-solving, they've still got a ways to go. But that distance could be quickly shortening, thanks to the emergence of a next-generation of A.I. called neuromorphic computing. Instead of teaching A.I.'s rigid logic gates and processors to learn through strict rules and datasets, neuromorphic computing takes a more biological approach to learning and designs computing systems that mimic a human brain's architecture of neurons and synapses.

A new era of computing just got closer, as researchers have created the design and run the first ever practical test for an artificial synapse that could let computers replicate some of the brain's most powerful and intricate functions. While computers might seem more powerful than our brains, we can actually deal with a much wider range of possible signals than the "on" and "off" of binary, thanks to the synapses that handle the connections between neurons. Replicating that capability in a computer requires artificial synapses that can reliably send all those subtly different signals. As they describe in Monday's issue of the journal Nature Materials, researchers at the Massachusetts Institute of Technology have performed what they call the first ever practical test of such an artificial synapse, unleashing what's known as neuromorphic computing. While the tests only happened in computer simulations, the tests were promising.