Inspired by the brain, researchers build smarter, more efficient computer hardware

As traditional computer chips reach their physical limits and artificial intelligence demands more energy than ever, University of Missouri researchers are rethinking how computers work by taking cues from the human brain.

The timing is critical. Energy use from AI data centers is projected to double by the end of the decade, raising urgent questions about sustainability.

The solution may lie in neuromorphic computing, an approach that reimagines computer hardware to process information more like biological neural networks rather than conventional chips.

“One of the brain’s greatest advantages is its efficiency,” Suchi Guha, a professor of physics in Mizzou’s College of Arts and Science, said. “It performs incredibly complex tasks using about 20 watts of power — roughly the same as an old light bulb. By comparison, today’s computer architecture is extremely energy-intensive.”

Making neuromorphic computing a reality starts at the hardware level. Guha and her team are developing electronic components designed to function like the connections between neurons that allow the brain to learn, adapt and store information — laying the groundwork for computers that are not only more powerful, but dramatically more efficient.

Rethinking the computer chip

For decades, computers have relied on transistors — tiny electronic switches that let machines process information. In most modern chips, however, thinking and memory happen in separate places. Every time a computer runs a task, data must shuttle back and forth between those two areas, which slows performance and burns energy.

The brain takes a different approach. Instead of separating memory and processing, individual connections between neurons — called synapses — do both at the same time. That setup allows the brain to learn and adapt while using surprisingly little energy.

Guha’s team is borrowing that idea for electronics. They are developing organic transistors that can both store and process information in the same place, much like synapses do in the brain.

“We’re not just trying to make faster transistors,” Guha, who is also a core faculty member with the MU Materials Science and Engineering Institute, said. “We’re trying to make devices that behave more like the brain itself.”

To see how well the approach works, the researchers tested several organic materials that looked almost identical on the surface. But once those materials were built into synaptic transistors, their performance differed dramatically.

The key factor turned out to be the interface — the thin boundary where the semiconductor meets an insulating layer inside the device.

“This shows us that performance isn’t just about what a material is made of,” Guha said. “It’s also about how it interacts with everything around it. Even small structural differences can have a big impact.”

Moving toward energy‑efficient, brain‑like AI

By clarifying how molecular design and interface quality influence synaptic behavior, Mizzou’s work provides other researchers with guiding principles for building more effective neuromorphic hardware. Such systems could eventually lead to brain-like AI that learns more efficiently, consumes far less power and excels at tasks such as pattern recognition and decision-making.

While brain-inspired computing is still in its early stages, Guha said advances such as hers are narrowing the gap between biology and machines.

“The brain remains the gold standard for efficient computation,” she said. “If we want truly intelligent machines, we have to start building hardware that learns the way biology does.”

The study, “Structure–Function Coupling in Pyridyl Triazole Copolymers for Neuromorphic Synaptic Transistors,” was published in ACS Applied Electronic Materials. Co-authors are Arash Ghobadi, Abhijeet Abhi, Thomas Kallos, Dillan Gamachchi, Indeewari Karunarathne, Andrew Meng, Jospeh Mathai, Shubhra Gangopadhyay and Steven Kelley at Mizzou; and Salahuddin Attar and Mohammed Al-Hashimi at Hamad Bin Khalifa University.