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๐ฎ๐ญ๐ฎ๐ซ๐ ๐จ๐ ๐๐๐๐ฅ๐ญ๐ก๐๐๐ซ๐ ๐ฐ๐ข๐ญ๐ก ๐๐๐ฎ๐ซ๐จ๐ฆ๐จ๐ซ๐ฉ๐ก๐ข๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐
Neuromorphic computing is an exciting field aiming to replicate the intricate workings of the human brain in electronic devices. Unlike traditional digital chips, neuromorphic chips use artificial neurons and synapses to process information with natural analog signals. These chips have the potential to transform healthcare, especially in creating point-of-care devices for disease detection.
However, a significant challenge in neuromorphic computing is training the neural network on the chip, which can be time-consuming and energy-inefficient. Researchers from Eindhoven University of Technology and Northwestern University have introduced a groundbreaking neuromorphic biosensor capable of on-chip learning by processing real-time patient data. This innovation eliminates the need for external training, making the biosensor more adaptable and responsive.
To showcase its effectiveness, the researchers used the biosensor to diagnose cystic fibrosis, an inherited condition affecting the lungs and digestive system. The biosensor detects cystic fibrosis by measuring chloride anion levels in sweat samples. It uses an organic electrochemical transistor to sense these anions and a neuromorphic chip to process the signal and classify it as normal or abnormal, adjusting its decision threshold with each sample.
The results show that this biosensor achieves high accuracy and sensitivity in cystic fibrosis detection while consuming minimal power and providing rapid response times. Its flexibility and biocompatibility also make it suitable for wearable applications. This promising study paves the way for intelligent biosensors and personalized healthcare devices grounded in neuromorphic computing.
For more details, you can access the published study in Nature Electronics through this link
wccftech.com
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