'Artificial synapse' could make neural networks work more like brains

A resistor that works in a similar way to nerve cells in the body could be used to build neural networks for machine learning. Many large machine learning models rely on increasing amounts of processing power to achieve their results, but this has vast energy costs and produces large amounts of heat. One proposed solution is analogue machine learning, which works like a brain by using electronic devices similar to neurons to act as the parts of the model. However, these devices have so far not been fast, small or efficient enough to provide advantages over digital machine learning. Murat Onen at the Massachusetts Institute of Technology and his colleagues have created a nanoscale resistor that transmits protons from one terminal to another.