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Engineering News<br>ResearchAI Gets a Cerebellum<br>Overlooked brain region helps AI ignore the ordinary for more efficient computing

ResearchAI Gets a Cerebellum<br>Overlooked brain region helps AI ignore the ordinary for more efficient computing

Jul 10, 2026Amanda Morris

The Problem<br>Conventional AI continuously analyzes incoming data even when nothing has changed, wasting energy on unnecessary computation.

Our Idea<br>Researchers developed a cerebellum-inspired memtransistor that ignores expected inputs and rapidly detects unexpected events while using far less energy than conventional AI.

Why It Matters<br>This approach could enable fast, always-on AI for applications like wearable health monitors, autonomous vehicles, robotics, and cybersecurity without the high energy demands of today's systems.

Our Team<br>Professor Mark Hersam, Research associate professor Vinod K. Sangwan

The brain’s cerebellum doesn’t waste energy analyzing every moment. Instead, it constantly monitors the world for the unexpected—and springs into action only when something suddenly changes.

Inspired by this remarkably efficient strategy, Northwestern researchers developed a new brain-like electronic device that consumes very little energy and detects novelties almost instantly. In proof-of-concept experiments, the device identified abnormal heart rhythms within one-fifth of a heartbeat and with more than 98 percent accuracy. The device also required roughly 10,000 times fewer computer operations than conventional AI approaches—paving the way for more energy-efficient AI.

The breakthrough could enable a new generation of low-power, always-on AI systems for wearable health monitors, self-driving automobiles, autonomous robots, and cybersecurity systems that need to instantaneously recognize and react to unusual events without relying on massive, energy-hungry data centers.

The study was published July 10 in the journal Nature Communications.

"In the world of brain-like computing, researchers typically try to mimic the cerebrum, which is often viewed as the brain’s ‘thought center,’" said Northwestern Engineering’s Mark C. Hersam, who co-led the study. "In our work, we developed a device that mimics the cerebellum, which controls reflex reactions seemingly without even thinking. The cerebellum is excellent at ignoring the expected and reserving its resources for reacting to the unexpected. That approach ultimately translates into lower energy consumption, and that is where we achieve orders of magnitude improvement."

An expert in brain-like computing, Hersam is the Walter P. Murphy Professor of Materials Science and Engineering, professor of medicine, and professor of chemistry at Northwestern, where he has appointments in the McCormick School of Engineering, Northwestern University Feinberg School of Medicine, and Weinberg College of Arts and Sciences. He also is the chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center, and member of the International Institute for Nanotechnology. Hersam co-led the study with Vinod K. Sangwan, a research associate professor at McCormick; Indira M. Raman, the Bill and Gayle Cook Professor of Neurobiology at Weinberg; and Amit Trivedi, an associate professor of electrical and computer engineering at the University of Illinois Chicago.

In the world of brain-like computing, researchers typically try to mimic the cerebrum, which is often viewed as the brain’s ‘thought center.’ In our work, we developed a device that mimics the cerebellum, which controls reflex reactions seemingly without even thinking.<br>Mark C. Hersam Walter P. Murphy Professor and Chair of Materials Science and Engineering

Moving beyond classification

The new device represents the latest advance in Hersam’s lab’s broader effort to rethink AI hardware from the ground up. Conventional computers constantly shuttle data back and forth between physically separate memory and processors—a process that consumes a significant amount of energy. Hersam’s group instead collapses memory and computation into a single device called a memtransistor.

In a 2023 study published in Nature Electronics, the team demonstrated that just two memtransistors could perform AI classification tasks that otherwise required more than 100 conventional transistors. That approach reduced energy consumption by roughly 100-fold.

The new study pushes that concept beyond low-energy classification. Rather than simply making AI hardware more efficient, the Northwestern team redesigned the device to mimic a specific circuit in the cerebellum, which excels at detecting novelties and making split-second decisions.

The approach allows AI to ignore routine information while immediately flagging unexpected events. For wearable...