Designed by researchers at Northwestern University, Boston College and the Massachusetts Institute of Technology (MIT), the device simultaneously processes and stores information just like the human brain. In new experiments, the researchers demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and is capable of performing associative learning.

Although previous studies have leveraged similar strategies to develop brain-like computing devices, those transistors cannot function outside cryogenic temperatures. The new device, by contrast, is stable at room temperatures. It also operates at fast speeds, consumes very little energy and retains stored information even when power is removed, making it ideal for real-world applications.

The study will be published on Wednesday (Dec. 20) in the journal Nature.

“The brain has a fundamentally different architecture than a digital computer,” said Northwestern’s Mark C. Hersam, who co-led the research. “In a digital computer, data move back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain.”

Hersam is the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern’s McCormick School of Engineering. He also is chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center and member of the International Institute for Nanotechnology. Hersam co-led the research with Qiong Ma of Boston College and Pablo Jarillo-Herrero of MIT.

Recent advances in artificial intelligence (AI) have motivated researchers to develop computers that operate more like the human brain. Conventional, digital computing systems have separate processing and storage units, causing data-intensive tasks to devour large amounts of energy. With smart devices continuously collecting vast quantities of data, researchers are scrambling to uncover new ways to process it all without consuming an increasing amount of power. Currently, the memory resistor, or “memristor,” is the most well-developed technology that can perform combined processing and memory function. But memristors still suffer from energy costly switching.

“For several decades, the paradigm in electronics has been to build everything out of transistors and use the same silicon architecture,” Hersam said. “Significant progress has been made by simply packing more and more transistors into integrated circuits. You cannot deny the success of that strategy, but it comes at the cost of high power consumption, especially in the current era of big data where digital computing is on track to overwhelm the grid. We have to rethink computing hardware, especially for AI and machine-learning tasks.”

To rethink this paradigm, Hersam and his team explored new advances in the physics of moiré patterns, a type of geometrical design that arises when two patterns are layered on top of one another. When two-dimensional materials are stacked, new properties emerge that do not exist in one layer alone. And when those layers are twisted to form a moiré pattern, unprecedented tunability of electronic properties becomes possible.

For the new device, the researchers combined two different types of atomically thin materials: bilayer graphene and hexagonal boron nitride. When stacked and purposefully twisted, the materials formed a moiré pattern. By rotating one layer relative to the other, the researchers could achieve different electronic properties in each graphene layer even though they are separated by only atomic-scale dimensions. With the right choice of twist, researchers harnessed moiré physics for neuromorphic functionality at room temperature.

“With twist as a new design parameter, the number of permutations is vast,” Hersam said. “Graphene and hexagonal boron nitride are very similar structurally but just different enough that you get exceptionally strong moiré effects.”

To test the transistor, Hersam and his team trained it to recognize similar — but not identical — patterns. Just earlier this month, Hersam introduced a new nanoelectronic device capable of analyzing and categorizing data in an energy-efficient manner, but his new synaptic transistor takes machine learning and AI one leap further.

“If AI is meant to mimic human thought, one of the lowest-level tasks would be to classify data, which is simply sorting into bins,” Hersam said. “Our goal is to advance AI technology in the direction of higher-level thinking. Real-world conditions are often more complicated than current AI algorithms can handle, so we tested our new devices under more complicated conditions to verify their advanced capabilities.”

First the researchers showed the device one pattern: 000 (three zeros in a row). Then, they asked the AI to identify similar patterns, such as 111 or 101. “If we trained it to detect 000 and then gave it 111 and 101, it knows 111 is more similar to 000 than 101,” Hersam explained. “000 and 111 are not exactly the same, but both are three digits in a row. Recognizing that similarity is a higher-level form of cognition known as associative learning.”

In experiments, the new synaptic transistor successfully recognized similar patterns, displaying its associative memory. Even when the researchers threw curveballs — like giving it incomplete patterns — it still successfully demonstrated associative learning.

“Current AI can be easy to confuse, which can cause major problems in certain contexts,” Hersam said. “Imagine if you are using a self-driving vehicle, and the weather conditions deteriorate. The vehicle might not be able to interpret the more complicated sensor data as well as a human driver could. But even when we gave our transistor imperfect input, it could still identify the correct response.”

The study, “Moiré synaptic transistor with room-temperature neuromorphic functionality,” was primarily supported by the National Science Foundation.

With its exquisite sensitivity, Webb captured Uranus’ dim inner and outer rings, including the elusive Zeta ring — the extremely faint and diffuse ring closest to the planet. It also imaged many of the planet’s 27 known moons, even seeing some small moons within the rings.

In visible wavelengths as seen by Voyager 2 in the 1980s, Uranus appeared as a placid, solid blue ball. In infrared wavelengths, Webb is revealing a strange and dynamic ice world filled with exciting atmospheric features.

One of the most striking of these is the planet’s seasonal north polar cloud cap. Compared to the Webb image from earlier this year, some details of the cap are easier to see in these newer images. These include the bright, white, inner cap and the dark lane in the bottom of the polar cap, toward the lower latitudes.

Several bright storms can also be seen near and below the southern border of the polar cap. The number of these storms, and how frequently and where they appear in Uranus’s atmosphere, might be due to a combination of seasonal and meteorological effects.

The polar cap appears to become more prominent when the planet’s pole begins to point toward the Sun, as it approaches solstice and receives more sunlight. Uranus reaches its next solstice in 2028, and astronomers are eager to watch any possible changes in the structure of these features. Webb will help disentangle the seasonal and meteorological effects that influence Uranus’s storms, which is critical to help astronomers understand the planet’s complex atmosphere.

Because Uranus spins on its side at a tilt of about 98 degrees, it has the most extreme seasons in the solar system. For nearly a quarter of each Uranian year, the Sun shines over one pole, plunging the other half of the planet into a dark, 21-year-long winter.

With Webb’s unparalleled infrared resolution and sensitivity, astronomers now see Uranus and its unique features with groundbreaking new clarity. These details, especially of the close-in Zeta ring, will be invaluable to planning any future missions to Uranus.

Uranus can also serve as a proxy for studying the nearly 2,000 similarly sized exoplanets that have been discovered in the last few decades. This “exoplanet in our backyard” can help astronomers understand how planets of this size work, what their meteorology is like, and how they formed. This can in turn help us understand our own solar system as a whole by placing it in a larger context.

These new findings underscore the urgency of addressing adverse childhood experiences (ACEs) — potentially traumatic events that occur before 18 years of age — and taking steps to mitigate their long-term impact on people’s health.

The study reviews research carried out across 75 years, involving 826,452 adults. Published in the peer-reviewed journal European Journal of Psychotraumatology, it reveals that individuals who have been exposed to various forms of traumatic events in childhood are at an increased risk of experiencing chronic pain and pain-related disability in adulthood, particularly those subjected to physical abuse. The cumulative impact of exposure to multiple ACEs further exacerbates this risk.

“These results are extremely concerning, particularly as over 1 billion children — half of the global child population — are exposed to ACEs each year, putting them at increased risk of chronic pain and disability later in life,” says lead author Dr André Bussières, from the School of Physical & Occupational Therapy at McGill University, in Canada.

“There is an urgent need to develop targeted interventions and support systems to break the cycle of adversity and improve long-term health outcomes for those individuals who have been exposed to childhood trauma.”

ACEs may affect a child or teenager directly through physical, sexual, or emotional abuse, or neglect — or indirectly through exposure to environmental factors like domestic violence, living with substance abuse or parental loss. Chronic pain, affecting between one-third and one-half of the UK population alone, is one of the leading causes of disability worldwide. Long-term painful conditions such as low back pain, arthritis, headache and migraine, can affect a person’s daily functioning to the point they can’t work, eat properly, or participate in physical activities.

Previous research has indicated a positive relationship between exposure to ACEs and chronic pain in adulthood. However, there are still gaps in knowledge — particularly around which type of ACEs are associated with specific pain-related conditions, or whether a dose-response relationship exists.

To help address these gaps, the authors carried out a systematic review that included 85 studies. Of those, results from 57 studies could be pooled in meta-analyses. They found that:

• Individuals exposed to a direct ACE, whether physical, sexual, or emotional abuse, or neglect, were 45% more likely to report chronic pain in adulthood compared to those not exposed.

“These results underscore the urgency of addressing ACES, particularly in light of their prevalence and health repercussions,” says the senior author Professor Jan Hartvigsen, from the University of Southern Denmark.

“A more nuanced understanding of the precise relationship between ACEs and chronic pain will empower healthcare professionals and policymakers to devise targeted strategies to help diminish the long-term impact of early-life adversity on adult health.”

The authors propose that future research should delve into the biological mechanisms through which ACEs affect health across the lifespan, aiming to deepen understanding and develop ways to mitigate their impact.