In a fascinating step toward brain-inspired machines, scientists from an Australian university have crafted a neuromorphic device that thinks like the human mind. This compact system can observe hand motions, remember them, and process what it sees in real time, all without depending on an external computer.
Created at RMIT University, this innovation could spark a revolution in intelligent machines. Think self-driving cars, lifelike robots, and interactive systems that respond to us like fellow humans. As Professor Sumeet Walia, the lead researcher, explained, “This proof-of-concept device mimics the human eye’s ability to capture light and the brain’s ability to process that visual information, enabling it to sense a change in the environment instantly and make memories without the need for using huge amounts of data and energy.”
The core of this brain-like machine lies in a wonder material called molybdenum disulfide (MoS₂). This ultra-thin metal compound contains tiny defects at the atomic level that allow it to detect light and convert it into brain-style electric signals. When exposed to light, it mimics how our neurons charge, fire, and reset—just like in the leaky integrate and fire (LIF) model used in neuromorphic science.
Instead of taking pictures frame by frame, brain-like machine senses movement by capturing edges in the scene. It then stores this activity as memory, saving both energy and data. Using spiking neural networks (SNNs), the system learned to recognize both static and moving visuals with up to 80 percent accuracy after training.
PhD researcher Thiha Aung, co-author of the study, said, “We demonstrated that atomically thin molybdenum disulfide can accurately replicate the leaky integrate-and-fire (LIF) neuron behaviour, a fundamental building block of spiking neural networks.”
This discovery could greatly enhance how machines react in fast-changing environments, like on roads or factory floors. The team is now scaling up the single-pixel model to build arrays that handle more complex visual tasks, while also working on expanding its vision into infrared for smart environmental monitoring.
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