Australian engineers develop brain-like device for next-gen visual processing

RMIT University researchers have created a breakthrough “neuromorphic” device that can detect hand movements, store memories, and process information like a human brain—all without requiring an external computer.

This tiny innovation, which mimics how our eyes capture light and our brains process visual information, represents a significant advancement toward enabling instant visual processing in autonomous vehicles and advanced robotics.

“Neuromorphic vision systems are designed to use similar analogue processing to our brains, which can greatly reduce the amount of energy needed to perform complex visual tasks compared with digital technologies used today,” said Professor Sumeet Walia, Director of the RMIT Centre for Opto-electronic Materials and Sensors.

The device utilises molybdenum disulfide (MoS2), a metal compound whose atomic-scale defects can be harnessed to capture light and process it as electrical signals, similar to neurons in the brain. During experiments, it detected changes in a waving hand’s movement without capturing events frame by frame—a process known as edge detection that requires significantly less data processing and power.

PhD scholar Thiha Aung, the study’s first author, explained: “We demonstrated that atomically thin molybdenum disulfide can accurately replicate the leaky integrate-and-fire neuron behaviour, a fundamental building block of spiking neural networks.”

The technology could dramatically improve response times in automated vehicles and robotic systems, potentially saving lives in dangerous situations by detecting scene changes almost instantly.

The team is now scaling up their proof-of-concept single-pixel device to a larger pixel array with funding from an Australian Research Council Linkage Infrastructure, Equipment and Facilities grant. They’ve also filed a provisional patent for the work.

“While our system mimics certain aspects of the brain’s neural processing, particularly in vision, it’s still a simplified model,” noted Professor Walia. “We see our work as complementary to traditional computing, rather than a replacement.”

Picture: Supplied