Using a biosensor to detect cystic fibrosis as the test case, TU/e researchers have devised an innovative way to train neuromorphic chips as presented in a new paper in Nature Electronics.

Neuromorphic computers—which are based on the structure of the human brain—could revolutionize our future health care devices. However, their widespread use is hindered by the need to train neuromorphic computers using external training software, which can be time-consuming and energy inefficient.

Researchers from Eindhoven University of Technology and Northwestern University in the U.S. have developed a new neuromorphic biosensor capable of on-chip learning that doesn't need external training. As a proof-of-concept, the researchers used the biosensor to diagnose based on sweat samples.

"We have demonstrated that we can create a 'smart biosensor' that could learn to detect a disease, such as cystic fibrosis, without using a computer or software." That's how Eveline van Doremaele summarized their new paper with Yoeri van de Burgt from TU/e, as well as Xudong Ji and Jonathan Rivnay from Northwestern University in the U.S.\

To read more, click here.