Printed Artificial Neurons Stimulate Natural Brain Circuits

Brain-machine interfaces depend on artificial signals that can engage living neural circuits, yet many devices produce simplified outputs that fail to trigger realistic activity. These limitations slow progress toward neuroprosthetics for hearing, vision, and movement, and constrain efforts to build energy‑efficient neuromorphic systems. Engineers also need approaches that are manufacturable and biocompatible. To help address this challenge, researchers have now developed printed artificial neurons that directly communicate with living brain cells.

Northwestern University engineers created flexible, low‑cost artificial neurons that generate electrical signals realistic enough to activate biological tissue. The devices were validated on mouse brain slices, where they induced responses in real neurons. The work points to electronics that can communicate with the nervous system and could support future neuroprosthetic applications.

The devices use soft, printable materials designed to better mimic the structure and behavior of brain tissue. Electronic inks made from nanoscale flakes of molybdenum disulfide, a semiconductor, and graphene, an electrical conductor, are deposited onto flexible polymer substrates using aerosol jet printing. After printing, partial decomposition of the stabilizing polymer concentrates current into a narrow conductive filament, producing a sudden neuron-like response. The resulting artificial neurons can generate single spikes, continuous firing, and bursting patterns that closely resemble biological signaling.

For biological interfacing tests, investigators from Northwestern’s Weinberg College of Arts and Sciences applied the artificial neuron outputs to slices of mouse cerebellum. The artificial voltage spikes matched key biological features, including the timing and duration of living neuron spikes. These inputs reliably triggered activity in real neurons and activated circuits in a manner similar to natural signals.

The team reports that capturing diverse signaling patterns allows each artificial neuron to encode more information, which could reduce component counts and improve computing efficiency. The additive printing process places material only where needed, which simplifies manufacturing, lowers cost, and reduces waste. The findings were published on April 15 in Nature Nanotechnology and were supported by the National Science Foundation.

“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, Walter P. Murphy Professor of Materials Science and Engineering at Northwestern’s McCormick School of Engineering.

"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,” said Hersam.

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