Researchers at Northwestern University have created printed artificial neurons that can directly communicate with real brain cells, opening a path toward brain-machine interfaces and far more energy-efficient computing systems. In experiments using mouse brain tissue, these flexible, low-cost devices produced electrical signals that closely matched those generated by living neurons, successfully triggering responses in biological brain cells .

Why Is the Brain So Much More Efficient Than Computers?

Modern computers handle increasing workloads by packing billions of identical transistors onto rigid, two-dimensional silicon chips. Once manufactured, these systems remain fixed and unchanging. The human brain operates on completely different principles. It consists of many types of neurons, each with specialized roles, arranged in soft, three-dimensional networks that constantly change and adapt as learning occurs .

This fundamental difference in architecture translates to dramatic differences in energy consumption. The brain is approximately five orders of magnitude, or 100,000 times, more energy efficient than a digital computer when performing complex tasks. As artificial intelligence (AI) systems grow increasingly demanding, this efficiency gap has become a critical problem. Large data centers already consume vast amounts of power and require significant water for cooling, straining both energy grids and water supplies .

"The way you make AI smarter is by training it on more and more data. This data-intensive training leads to a massive power-consumption problem. Therefore, we have to come up with more efficient hardware to handle big data and AI. Because the brain is five orders of magnitude more energy efficient than a digital computer, it makes sense to look to the brain for inspiration for next-generation computing," said Mark C. Hersam, who led the study.

Mark C. Hersam, Walter P. Murphy Professor of Materials Science and Engineering at Northwestern University

How Did Researchers Create Artificial Neurons That Work Like Real Ones?

The Northwestern team built artificial neurons using soft, printable materials that more closely match the brain's structure than traditional silicon-based approaches. Their breakthrough involved using electronic inks made from nanoscale flakes of molybdenum disulfide, which acts as a semiconductor, and graphene, which serves as an electrical conductor. These materials were deposited onto flexible polymer surfaces using aerosol jet printing .

The key innovation came from how the researchers treated the polymer in these inks. Previous researchers had removed the polymer after printing because it interfered with electrical performance. Instead, the Northwestern team partially decomposed the polymer and then drove further decomposition by passing current through the device. This decomposition occurred in a spatially uneven manner, creating a narrow conductive filament that produces electrical responses similar to a neuron firing .

Steps to Understanding How These Artificial Neurons Function

• Material Composition: The artificial neurons use molybdenum disulfide as a semiconductor and graphene as a conductor, printed onto flexible polymer substrates using aerosol jet printing technology.
• Signal Generation: The devices produce a wide variety of electrical signals, including single spikes, continuous firing, and bursting patterns that closely resemble real neural communication.
• Biological Compatibility: The electrical signals match key biological properties of real neurons, including their timing and duration, allowing them to reliably activate living brain cells.
• Energy Efficiency: Because each artificial neuron can produce complex signals, fewer components are needed to perform advanced tasks, significantly improving computing efficiency.

The resulting artificial neurons can generate a wide variety of signals that closely resemble real neural communication. Because each artificial neuron can produce more complex signals on its own, fewer components are needed to perform advanced tasks. This could significantly improve computing efficiency compared to traditional silicon-based systems that require billions of identical transistors .

What Happens When Artificial Neurons Meet Living Brain Tissue?

To evaluate whether the artificial neurons could truly interact with living systems, the researchers partnered with neurobiology experts at Northwestern. Their team applied the artificial signals to slices of mouse cerebellum tissue. The results demonstrated that the electrical spikes matched key biological properties, including their timing and duration. These signals reliably activated real neurons and triggered neural circuits in a way similar to natural brain activity .

"Other labs have tried to make artificial neurons with organic materials, and they spiked too slowly. Or they used metal oxides, which are too fast. We are within a temporal range that was not previously demonstrated for artificial neurons. You can see the living neurons respond to our artificial neuron. So, we've demonstrated signals that are not only the right timescale but also the right spike shape to interact directly with living neurons," explained Mark C. Hersam.

Mark C. Hersam, Walter P. Murphy Professor of Materials Science and Engineering at Northwestern University

This breakthrough represents a significant advance in brain-machine interface technology. Potential applications include neuroprosthetics such as implants that could help restore hearing, vision, or movement in people with neurological injuries or degenerative conditions. The technology also points toward a new generation of computing systems inspired by the brain's architecture and efficiency .

Why Does This Matter for the Future of AI?

The energy demands of artificial intelligence systems are reaching unsustainable levels. Tech companies are already building gigawatt-scale data centers powered by dedicated nuclear power plants to meet current AI computing needs. The massive power consumption and heat dissipation from these facilities create severe environmental and infrastructure challenges .

Beyond performance advantages, the new approach offers significant environmental and practical benefits. The manufacturing process is simple and inexpensive, and the additive printing method places material only where it is needed, reducing waste compared to traditional semiconductor manufacturing. This scalable, sustainable approach could help address the growing energy crisis in AI computing .

"To meet the energy demands of AI, tech companies are building gigawatt data centers powered by dedicated nuclear power plants. It is evident that this massive power consumption will limit further scaling of computing since it's hard to imagine a next-generation data center requiring 100 nuclear power plants. The other issue is that when you're dissipating gigawatts of power, there's a lot of heat. Because data centers are cooled with water, AI is putting severe stress on the water supply. However you look at it, we need to come up with more energy-efficient hardware for AI," noted Mark C. Hersam.

Mark C. Hersam, Walter P. Murphy Professor of Materials Science and Engineering at Northwestern University

The study, published in Nature Nanotechnology in April 2026, represents a fundamental shift in how researchers think about computing hardware. By moving away from rigid, two-dimensional silicon architectures toward soft, three-dimensional, heterogeneous systems inspired by the brain, scientists may finally unlock the energy efficiency gains needed to sustain the next generation of artificial intelligence .