A study on the neurons titled "Nanosecond protonic programmable resistors for analog deep learning" has been published in the journal Science.

Researchers from the Massachusetts Institute of Technology (MIT) have created new artificial "neurons" and "synapses" that exist within a new field of artificial intelligence called analog deep learning. Instead of using transistors like in digital processors, analog deep learning uses programmable resistors to "create a network of analog artificial 'neurons' and 'synapses'" that can exceed the performance of a digital neural network, while using a fraction of the energy.

The MIT team's artificial neurons and synapses are built using a new inorganic material in their fabrication process, boosting the performance of devices using them to one million times faster than previous iterations and one million times faster than the synapses found in the human brain. The new material can also be used with existing silicon fabrication techniques, meaning it can be used to create nanometer-scale devices and potentially integrate the technology with existing computing hardware to facilitate deep-learning applications.

"Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft," said lead author and MIT postdoc Murat Onen.

"The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn't damage anything, thanks to the small size and low mass of protons. It is almost like teleporting. The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field," said senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering.

You can read more from the study here, and from MIT's breakdown here.