As AI demands ever more power, researchers are looking to the brain for more efficient ways to process information. A new approach uses soft, flexible electronics to create artificial neurons that can mimic biological signaling and even directly interface with living neural tissue.

Researchers have long attempted to create so-called “neuromorphic” chips made of artificial neurons that mimic the spiking behavior of their biological counterparts. But there are still wide gaps between how these devices and brains operate.

Real neurons in the brain display a wide variety of activity patterns, which helps them encode and process information extremely efficiently. In contrast, most artificial neurons are carbon copies of each other with highly uniform spiking behavior, forcing neuromorphic chips to use millions of these neurons to achieve even modest functionality.

Now, a team from Northwestern University has designed a novel fabrication technique to create artificial neurons that mimic the complex signaling patterns found in the brain. The neurons’ output was so realistic that they successfully stimulated neurons in mouse brain tissue. More importantly, the approach could lay the groundwork for much more energy efficient AI.

“Silicon achieves complexity by having billions of identical devices,” Mark Hersam, who co-led the research, said in a press release. “Everything is the same, rigid and fixed once it’s fabricated. The brain is the opposite. It’s heterogeneous, dynamic and three-dimensional. To move in that direction, we need new materials and new ways to build electronics.”

The team created their artificial neurons, described in a paper in Nature Nanotechnology, by jet printing special electronic ink onto a flexible polymer. The ink contains nanoscale flakes of molybdenum disulfide, which acts as a semiconductor, and graphene, which serves as an electrical conductor.

The ink also contains a stabilizing polymer researchers typically burn off after printing to prevent it from interfering with the flow of current. But the researchers discovered that by leaving some of it behind, they could introduce imperfections that result in far more sophisticated signaling behavior.

Rather than completely burning the material away, they partially decomposed it. Then when they passed a current through the printed neurons, the polymer broke down further, but in an uneven pattern that created a conductive thread where current gets squeezed into a tight channel.

This constricted pathway rapidly switches on and off, firing sharp voltage spikes that look a lot like the spikes found in real neurons. The device doesn’t just produce simple on-off pulses, but everything from isolated spikes to sustained firing to rhythmic bursts, much like a real neuron.

With just two of these printable neurons and some basic circuit components, the researchers produced sophisticated spiking patterns. And crucially, they were able to tune the length and frequency of spikes to match the timing of biological action potentials, which could be useful for applications like bioelectronic medicine or brain-computer interfaces.

To test whether they could go beyond simply matching the numbers, the team worked with Northwestern neurobiology professor, Indira Raman, to hook up their artificial neurons to slices of mouse cerebellum and fire spikes into the tissue. The biological neurons fired in response, showing the synthetic signals were convincing enough to activate real neural circuits.

“You can see the living neurons respond to our artificial neuron,” said Hersam. “So, we’ve demonstrated signals that are not only the right timescale but also the right spike shape to interact directly with living neurons.”

While those capabilities could lead to some interesting applications, the researchers’ mainly hope the technology can reduce AI’s energy bill by mimicking the brain’s more efficient processing.

“To meet the energy demands of AI, tech companies are building gigawatt data centers powered by dedicated nuclear power plants,” Hersam said. This can only scale so far, in terms of power and cooling, he said. “However you look at it, we need to come up with more energy-efficient hardware for AI.”

Given the long, tortuous path from lab bench to factory floor, it seems unlikely this technology will be making a dent in the industry’s power bill any time soon. But it could lay the groundwork for a smarter way to do computation in the future.