The human brain is five orders of magnitude more energy efficient

Health · Published 18 October 2026 Swipe more like this →

In short: Modern AI faces a looming power crisis, requiring massive data centers and dedicated nuclear plants to stay online. To solve this, engineers are looking to the human brain—the most efficient computing system known—to design a new generation of hardware that mimics biological neural networks.

Current silicon-based computing relies on billions of identical, rigid, two-dimensional transistors. This architecture is fixed once manufactured and lacks the dynamic, three-dimensional, and heterogeneous nature of biological brains. Northwestern University scientists are creating synthetic neurons to mimic biological cell interaction, as the brain operates with five orders of magnitude greater energy efficiency than digital systems. Silicon chips are limited by their static nature, whereas biological networks constantly adjust connections during learning. This structural difference is why researchers are moving toward new materials to build electronics that can interface directly with the nervous system.

To achieve this, engineers used aerosol jet printing to deposit electronic inks—made from molybdenum disulfide (MoS2) and graphene—onto flexible polymer surfaces. Unlike previous attempts that removed the polymer, this team partially decomposed it to create a conductive filament. The team utilized a specific semiconductor called molybdenum disulfide alongside graphene as an electrical conductor. This additive printing method is notably sustainable because it places material only where it is needed, which reduces waste during the manufacturing process.

This allows the artificial neurons to produce complex signals, such as single spikes and bursting patterns, that closely match the timing and duration of real neural activity. In tests using mouse cerebellum slices, these printed neurons successfully triggered responses in living brain tissue. This breakthrough offers a path toward more efficient AI hardware and advanced neuroprosthetics, such as implants to restore vision or movement. By replicating the brain's ability to perform complex tasks with fewer components, these devices could bypass the massive power-consumption and heat dissipation problems currently limiting the scaling of large-scale data centers. The study, scheduled for publication in Nature Nanotechnology on April 15, highlights how these signals matched key biological properties. Furthermore, these advancements could eventually lead to brain-inspired computing systems that perform complex tasks using significantly less energy than current digital models.

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