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Artificial signals can reliably trigger activity in living neural circuits, marking a major step for neuroprosthetics. Credit: Neuroscience News<\/p>\nSummary: Engineers have achieved a major breakthrough in bio-electronics: printing artificial neurons that can communicate directly with the biological brain. The study details the development of flexible, low-cost devices that generate electrical signals so realistic they successfully triggered responses in mouse brain tissue.<\/p>\n
Unlike traditional silicon chips, these \u201cprintable\u201d neurons mimic the brain\u2019s energy efficiency and signaling complexity, paving the way for advanced neuroprosthetics and AI hardware that consumes a fraction of the power used by modern data centers.<\/p>\n
Key Findings<\/p>\n
Biocompatibility in Action: In tests with the lab of Indira M. Raman, the artificial neurons were hooked up to slices of a mouse cerebellum. The living neurons responded to the artificial spikes as if they were coming from a biological peer.Energy Efficiency: The brain is five orders of magnitude more energy-efficient than a digital computer. By mimicking its structure, these devices could handle \u201cBig Data\u201d without the massive power and water cooling requirements of modern AI data centers.Structural Mimicry: Unlike silicon chips with billions of identical, fixed transistors, these artificial neurons are heterogeneous and dynamic, mimicking the soft, 3D changing networks of the human brain.Additive Manufacturing: The printing process is low-cost and environmentally friendly, only placing material where it is needed, which significantly reduces waste.<\/p>\n
Source: Northwestern University<\/p>\n
Northwestern University engineers printed artificial neurons that don\u2019t just imitate the brain, they talk to it.<\/p>\n
In a new study, the Northwestern team developed flexible, low-cost devices that generate electrical signals realistic enough to activate living brain cells. When tested on slices of tissue from mouse brains, the artificial neurons successfully triggered responses from real neurons, demonstrating a new level of biocompatibility.<\/p>\n
Artificial signals can reliably trigger activity in living neural circuits, marking a major step for neuroprosthetics. Credit: Neuroscience News<\/p>\n
The work marks a step toward electronics that can communicate directly with the nervous system, with potential applications in brain-machine interfaces and neuroprosthetics, including implants for hearing, vision and movement.\u00a0<\/p>\n
It also lays the groundwork for more efficient, brain-like computing systems. By mimicking how neurons signal, a key feature of the brain, which is the most energy-efficient computer known, futuristic systems could perform complex operations using far less power than today\u2019s data-hungry technologies.<\/p>\n
The study will be published on Wednesday (April 15) in the journal Nature Nanotechnology.<\/p>\n
\u201cThe world we live in today is dominated by artificial intelligence (AI),\u201d said Northwestern\u2019s\u00a0Mark C. Hersam, who led the study.<\/p>\n
\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
An expert in brain-like computing, Hersam is the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern\u2019s\u00a0McCormick School of Engineering, professor of medicine at Northwestern University\u00a0Feinberg School of Medicine\u00a0and professor of chemistry at Northwestern\u2019s\u00a0Weinberg College of Arts and Sciences. He also is the chair of the department of materials science and engineering, director of the\u00a0Materials Research Science and Engineering Center\u00a0and member of the\u00a0International Institute for Nanotechnology. Hersam co-led the study with\u00a0Vinod K. Sangwan, a research associate professor at McCormick.<\/p>\n
From rigid silicon to dynamic brains<\/p>\n
As computing tasks become more complex and data-intensive, computers meet these demands by adding more identical components \u2014 billions of transistors packed onto rigid, two-dimensional silicon chips. Each transistor behaves the same way. And, once fabricated, those systems remain fixed.<\/p>\n
The brain operates in a strikingly different way. Rather than comprising uniform building blocks, the brain relies on diverse types of neurons \u2014 each performing specialized roles \u2014 organized across regions. These soft, three-dimensional networks constantly change, forming and reshaping connections over time as people learn and adapt.<\/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
While other artificial neurons do exist, they fall short of biological realism. Most produce simplified signals, forcing engineers to rely on large, energy-intensive networks of devices to achieve complex behavior.<\/p>\n
Turning an imperfection into a feature<\/p>\n
To move closer to a biological model, Hersam\u2019s team developed artificial neurons using soft, printable materials that better mimic the brain\u2019s structure and behavior. The backbone of that advance is a series of electronic inks, formulated from nanoscale flakes of molybdenum disulfide (MoS2), which acts as a semiconductor, and graphene, which serves an electrical conductor. Using a specialized printing technique called aerosol jet printing, the researchers deposited these inks onto flexible polymer substrates.<\/p>\n
In the past, other researchers viewed the stabilizing polymer in the inks as a problem that interfered with electrical current flow, so they burned the polymer away after printing the electronic circuit. But Hersam leveraged this minor imperfection to add brain-like functionality to his device.<\/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 region becomes a localized pathway that produces a sudden, neuron-like electrical response. The result is a new type of artificial neuron that can generate a rich range of electrical signals.<\/p>\n
Instead of generating simple, one-off pulses, the new device produces more complex signaling patterns \u2014 including single spikes, continuous firing and bursting patterns \u2014 that resemble how real neurons communicate.<\/p>\n
By capturing this signaling diversity, each neuron can encode more information and perform more sophisticated functions. And that can reduce the number of components needed in a computing system, drastically improving overall efficiency.<\/p>\n
Putting artificial neurons to the test<\/p>\n
To test whether its artificial neurons truly could interface with biology, Hersam\u2019s team collaborated with\u00a0Indira M. Raman, the Bill and Gayle Cook Professor of Neurobiology at Weinberg. Raman\u2019s team applied electrical signals from the artificial neurons to slices of mouse cerebellum.<\/p>\n
They found the artificial voltage spikes matched key biological features, including timing and duration of living neuron voltage spikes. This reliably triggered activity in real neurons, activating neural circuits in a way similar to natural signals.<\/p>\n
\u201cOther labs have tried to make artificial neurons with organic materials, and they spiked too slowly,\u201d Hersam said.<\/p>\n
\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
The approach comes with several environmentally friendly advantages. In addition to improving energy efficiency, the neuron\u2019s manufacturing process is simple and low-cost. Because the printing process is additive \u2014 placing material only where it\u2019s needed \u2014 it also reduces waste.<\/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.<\/p>\n
\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.<\/p>\n
\u201cThe 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
Funding: The study, \u201cMulti-order complexity spiking neurons enabled by printed MoS2\u00a0memristive nanosheet networks,\u201d was supported by the National Science Foundation.<\/p>\n
Key Questions Answered:Q: Does this mean we can \u201cprint\u201d a new brain?<\/p>\n
A: Not quite, but we can print the \u201ctranslators.\u201d These artificial neurons act as a bridge. Because they speak the same electrical language as your body, they could be used to create better cochlear implants for hearing, visual prosthetics for the blind, or interfaces that allow paralyzed patients to move robotic limbs with their thoughts.<\/p>\n
Q: Why is \u201cwasting less power\u201d such a big deal for AI?<\/p>\n
A: Modern AI is a \u201cpower hog.\u201d Tech companies are currently looking into building dedicated nuclear power plants just to keep up with the data centers needed for tools like ChatGPT. By making hardware that works like a brain (which runs on about the same power as a dim lightbulb), we can make AI smarter without draining the world\u2019s energy and water.<\/p>\n
Q: How are these different from the \u201cneural networks\u201d already in my computer?<\/p>\n
A: Your computer\u2019s \u201cneural networks\u201d are just software, math code running on rigid metal. These are physical neural networks. They are soft, flexible, and have \u201cmemory\u201d built into their physical structure, making them much closer to the \u201cwetware\u201d inside your skull.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this neurotech research news<\/p>\n
Author:\u00a0Amanda Morris<\/a>
Source:\u00a0Northwestern University<\/a>
Contact:\u00a0Amanda Morris \u2013 Northwestern University
Image:\u00a0The image is credited to Neuroscience News<\/p>\n