{"id":381756,"date":"2026-05-01T03:02:00","date_gmt":"2026-05-01T01:02:00","guid":{"rendered":"https:\/\/prostartup.it\/scientists-print-artificial-neurons-that-can-talk-to-the-brain\/"},"modified":"2026-05-01T03:02:00","modified_gmt":"2026-05-01T01:02:00","slug":"scientists-print-artificial-neurons-that-can-talk-to-the-brain","status":"publish","type":"post","link":"https:\/\/prostartup.it\/ru\/scientists-print-artificial-neurons-that-can-talk-to-the-brain\/","title":{"rendered":"Scientists Print Artificial Neurons That Can Talk to the Brain"},"content":{"rendered":"
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\"Aerosol
An aerosol jet printer in Hersam\u2019s laboratory deposits electronic inks onto a flexible polymer substrate. Credit: Mark Hersam\/Northwestern University<\/figcaption><\/figure>\n

Printed artificial neurons can now send lifelike signals that activate real brain cells. This breakthrough could transform both brain implants and energy-efficient AI.<\/strong><\/p>\n

Engineers at Northwestern University<\/span> have developed printed artificial neurons that go beyond simple imitation and can directly engage with real brain cells. These devices are flexible, inexpensive, and capable of producing electrical signals that closely resemble those of living neurons.<\/p>\n

In laboratory tests using slices of mouse brain tissue, the artificial neurons successfully stimulated real neurons, prompting measurable responses. This achievement highlights a new level of compatibility between electronic systems and biological neural networks.<\/p>\n

Advancing Brain Interfaces and Low-Power Computing<\/h4>\n

This research represents an important step toward electronics that can communicate with the nervous system. Such technology could support brain-machine interfaces and neuroprosthetic devices, including implants designed to restore hearing, vision, or movement.<\/p>\n

The findings also point to a future of more efficient computing. By reproducing the way neurons send signals \u2014 a defining trait of the brain, which is the most energy-efficient computing system known \u2014 next-generation hardware could handle complex tasks while using far less energy than current systems.<\/p>\n

The study was published on April 15 in the journal Nature Nanotechnology<\/em>.<\/p>\n

\u201cThe world we live in today is dominated by artificial intelligence<\/span> (AI),\u201d said Northwestern\u2019s Mark C. Hersam, who led the study. \u201cThe 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.\u201d<\/p>\n

Hersam is a leading researcher in brain-inspired electronics and holds multiple roles at Northwestern University. He is the Walter P. Murphy Professor of Materials Science and Engineering at the McCormick School of Engineering, as well as a professor of medicine at Northwestern University Feinberg School of Medicine and a professor of chemistry at the Weinberg College of Arts and Sciences. He also serves as chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center, and a member of the International Institute for Nanotechnology. The study was co-led by Vinod K. Sangwan, a research associate professor at McCormick.<\/p>\n

From Traditional Silicon to Brain-Like Systems<\/h4>\n

As computing demands grow, conventional systems meet these challenges by adding more identical components. Modern chips contain billions of transistors arranged on rigid, flat silicon, with each element performing the same function. Once built, these systems do not change.<\/p>\n

The brain operates in a completely different way. It consists of many types of neurons, each with specialized roles, organized in soft, three-dimensional networks. These networks are constantly adapting, forming new connections and reshaping existing ones as learning occurs.<\/p>\n

\u201cSilicon achieves complexity by having billions of identical devices,\u201d Hersam said. \u201cEverything is the same, rigid and fixed once it\u2019s fabricated. The brain is the opposite. It\u2019s heterogeneous, dynamic, and three-dimensional. To move in that direction, we need new materials and new ways to build electronics.\u201d<\/p>\n

Although artificial neurons have been created before, most produce overly simple signals. To generate more complex behavior, engineers typically rely on large networks, which increases energy consumption.<\/p>\n

Printable Materials Enable More Realistic Neurons<\/h4>\n

To better match the behavior of real neurons, the researchers designed their devices using soft, printable materials. They created specialized electronic inks made from nanoscale<\/span> flakes of molybdenum disulfide (MoS2<\/sub>), which functions as a semiconductor, and graphene<\/span>, which acts as a conductor. These inks were deposited onto flexible polymer surfaces using a method known as aerosol jet printing.<\/p>\n

Previously, the polymer component in these inks was considered a drawback because it interfered with electrical flow, so it was usually removed after printing. In this case, the team used it to their advantage.<\/p>\n

\u201cInstead of fully removing the polymer, we partially decompose it,\u201d he said. \u201cThen, when we pass current through the device, we drive further decomposition of the polymer. This decomposition occurs in a spatially inhomogeneous manner, leading to formation of a conductive filament, such that all the current is constricted into a narrow region in space.\u201d<\/p>\n

This narrow conductive path creates a sudden electrical response similar to a neuron firing. As a result, the artificial neurons can produce a wide variety of signals, including single spikes, steady firing, and bursting patterns, closely mirroring real neural activity.<\/p>\n

Because each device can handle more complex signaling, fewer components are needed overall, which could significantly improve efficiency in future computing systems.<\/p>\n

Testing Artificial Neurons on Living Tissue<\/h4>\n

To determine whether these artificial neurons could interact with real biological systems, the team partnered with Indira M. Raman, the Bill and Gayle Cook Professor of Neurobiology at Weinberg. Her group applied the artificial signals to slices of mouse cerebellum.<\/p>\n

The results showed that the electrical spikes matched key features of natural neuron activity, including timing and duration. These signals reliably activated real neurons and triggered neural circuits in a way similar to natural brain signals.<\/p>\n

\u201cOther labs have tried to make artificial neurons with organic materials, and they spiked too slowly,\u201d Hersam said. \u201cOr 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\u2019ve demonstrated signals that are not only the right timescale but also the right spike shape to interact directly with living neurons.\u201d<\/p>\n

Efficient Manufacturing and Implications for AI<\/h4>\n

The new approach also offers environmental and practical benefits. The manufacturing process is simple and cost-effective, and the additive printing method uses materials efficiently by placing them only where needed, reducing waste.<\/p>\n

Improving energy efficiency is especially important as artificial intelligence systems continue to expand. Large data centers already consume enormous amounts of power and require significant water for cooling.<\/p>\n

\u201cTo meet the energy demands of AI, tech companies are building gigawatt data centers powered by dedicated nuclear power plants,\u201d Hersam said. \u201cIt is evident that this massive power consumption will limit further scaling of computing since it\u2019s hard to imagine a next-generation data center requiring 100 nuclear power plants. The other issue is that when you\u2019re dissipating gigawatts of power, there\u2019s 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.\u201d<\/p>\n

Reference: \u201cPrinted MoS2<\/sub> memristive nanosheet networks for spiking neurons with multi-order complexity\u201d by Shreyash S. Hadke, Carol N. Klingler, Spencer T. Brown, Meghana Holla, Xudong Zhuang, Linda Li, M. Iqbal Bakti Utama, Santiago Diaz-Arauzo, Anurag Chapagain, Siyang Li, Jung Hun Lee, Indira M. Raman, Vinod K. Sangwan and Mark C. Hersam, 15 April 2026, Nature Nanotechnology<\/i>.
DOI: 10.1038\/s41565-026-02149-6<\/p>\n

The study was supported by the National Science Foundation.<\/p>\n

Never miss a breakthrough: Join the SciTechDaily newsletter.<\/b>
Follow us on Google and Google News.<\/b><\/p>\n

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