Researchers from Eindhoven and Northwestern University have Developed a New Neuromorphic Biosensor Capable of On-Chip Learning that doesn’t need External Training

Neuromorphic computing is inspired by the human brain’s structure and function. A neuromorphic chip is a device that uses physical artificial neurons to do computations. Unlike conventional digital processors, these chips are designed to carry out artificial intelligence (AI) and machine learning (ML) activities in a biologically inspired and energy-efficient method. However, their widespread use could be improved by the need to train neuromorphic computers using external training software, which is time-consuming and energy-inefficient.

To tackle this issue, researchers from Eindhoven University of Technology and Northwestern University in the United States have formulated a neuromorphic biosensor capable of on-chip learning, eliminating the need for external training. 

The smart biosensor they used is a neuromorphic biosensing computer built to look like how neurons communicate in the human brain.

The researchers said a smart biosensor could learn to detect a disease like cystic fibrosis without using a computer or software. Further, they noted that Neuromorphic computing could significantly impact health care, particularly point-of-care devices to check for an illness or condition.

The researchers tested the effectiveness of their brand new chip on the genetic disease cystic fibrosis(a hereditary condition that can damage organs, such as the lungs and digestive system). Cystic fibrosis can be detected using a sweat test, where a high amount of chloride anions indicates the condition.

The researchers said they didn’t work with real patient data for ease of implementation. However, they used sweat samples from healthy donors. They used one donor sweat sample that was negative or healthy, and they prepared a second sample with a very high quantity of chloride anions.  The researchers said that they studied several sweat samples with varying and known ion concentrations and then tested the samples on the chip. If the result from the chip for a test was wrong, they corrected the chip.

The biosensor consists of three main parts—the sensor module, the hardware neural network, and the output classification part. The modular biosensor is an integrated array of organic neuromorphic devices that form the synaptic weights of a hardware neural network and an output classification layer. Ion-selective electrodes measure the amounts of chloride and other ions in the sweat after a drop of sweat is applied to the sensor module. The neuromorphic chip processes these impulses, and the analysis results are shown as a green or red light, signifying a successful or unsuccessful outcome.

The potential for individualized implantable neural networks that can be trained directly by the end user using their data is made possible by this on-chip learning methodology. A method like this holds the potential to have a big influence on people. It could eventually train chips to operate prosthetic limbs and other devices in real time. In contrast to conventional methods, these chips are truly unique in their capacity to learn from and adapt to their jobs and settings, doing away with the necessity for pre-programming.

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