Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Nature Climate Change, Published online: 18 August 2026; doi:10.1038/s41558-026-02676-z

Leading European cities race to reach net-zero emissions, but residual emissions are tied to easier-to-abate sectors and temporary, land-intensive carbon removal for compensation. To keep climate neutrality credible and fair, policy must tighten expectations on cutting emissions and set clear rules for carbon removal and credits.

A functional nervous system depends on the cooperation of many kinds of cells. So as developing organisms build their nervous systems, their neurons must take on different forms and functions to fulfill their designated roles. That carefully orchestrated process gives rise to thousands of different cell types in the human brain.

In the tiny worm known as C. elegans, the nervous system is far simpler, comprising a mere 118 classes of neurons.

At MIT, scientists in H. Robert Horvitz’s lab are studying the worms to learn about how nervous systems develop. Horvitz is the David H. Koch Professor of Biology at MIT, an investigator at the McGovern Institute for Brain Research at MIT, and an investigator at the Howard Hughes Medical Institute. His team has just discovered that a protein complex called cohesin, which helps shape the three-dimensional structure of the genome in both worms and humans, is critical for establishing some neurons’ identities as development unfolds.

The open-access findings, reported July 31 in the journal Science Advances, could help scientists find a way to treat a rare developmental disorder called Cornelia de Lange syndrome, which is caused by mutations that interrupt the cohesin complex.

Model organism

MIT postdoc Dongyeop Lee explains that C. elegans is a powerful model for studying neurodevelopment not just because its nervous system has been comprehensively mapped, but also because of the ease and speed with which scientists can study the function of its genes.

Because many of the worm’s genes have been retained through evolution, findings from studies of C. elegans often reveal important aspects of human biology. The current study began with worms that, because of a genetic mutation, make too many neurons of a certain type.

Adrenergic neurons, named for the kind of neurotransmitter they use to communicate with other neurons, are vital for enabling worms to respond to both their environment and their own internal state. Normally, C. elegans has just two pairs of adrenergic neurons: two RIM neurons and two RIC neurons. But the worms Lee studied had extras of both.

Takashi Hirose, a former member of the Horvitz lab, first observed this change in 2007.

Lee later continued the study and discovered that worms carrying a mutation in a gene called coh-1 have extra adrenergic neurons. The coh-1 gene encodes one part of the cohesin complex.

When Lee tested other mutations that disrupt cohesin, he found the same effect: Worms without fully functional cohesin had too many RIM neurons and too many RIC neurons.

Molecular switch

With a series of experiments designed to tease apart how cohesin impacts neurons’ identities, Lee discovered that cohesin cooperates with a gene-regulating protein called EOR-1 (known in humans as PLZF) to direct some neurons to develop into neurons that communicate with the inhibitory neurotransmitter GABA.

By reorganizing the structure of the genome, cohesin can change the way gene regulators like EOR-1 interact with DNA. Lee’s experiments showed that when either cohesin or EOR-1 couldn’t do its job, cells that should have become GABA-producing neurons become adrenergic neurons instead.

“What we found is that there are two alternative possible fates of certain neurons, and cohesin acts as a molecular switch that decides one of the possible neuronal fates,” Lee explains. “This means the structure of genomic DNA in the nucleus is important for neuronal fate determination.”

Disease connection

Lee adds that extra adrenergic neurons were not the only abnormality he observed in worms with cohesin mutations. Cohesin is important for shaping cells and tissues throughout the body. “The mutants have severe developmental defects,” Lee says. “They grow slowly. They don’t move well, and they also have defects in reproduction.”

Notably, the problems Lee saw in the worms echo aspects of Cornelia de Lange syndrome, a rare genetic disorder that impacts physical, cognitive, and behavioral development. Cornelia de Lange syndrome can be caused by mutations in cohesin genes, and Lee says that the discovery of how cohesin mutations affect worm development and behavior opens new opportunities to study the disease and search for potential therapeutic targets in C. elegans.

The Horvitz lab already has some promising leads. Taking advantage of the quick genetic screens that are possible in worms, Lee has found additional mutations that can counteract impaired cohesin, improving the health of worms with cohesin mutations. The team is now working to identify the genes where these suppressor mutations occur, so they can investigate whether they might make good therapeutic targets in humans.

Meanwhile, the team is also exploring a potential role for cohesin in shaping the fates of other neuron types, as well as searching broadly for additional molecules that work with cohesin to guide development. “We expect we have opened up a new biology,” Lee says. “This paper is just the beginning.”

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

The scales of a pine cone open in low humidity to scatter seeds, but close in damp conditions to protect seeds from moisture. An artificial material with the same behavior could be useful in applications like moisture-responsive shingles for passive cooling.

MIT researchers have now developed a system that simplifies the process of designing this type of bioinspired material. 

Their framework captures how mechanisms across length scales in a natural system, like the cells, fibers, and tissues inside a pine cone, work together to achieve unique properties. It then formally translates that behavior in an engineered system. 

The framework organizes biological behavior into building blocks that can be used to design synthetic structures that can be mathematically validated to perform the same way, and fabricated using a 3D printer. 

By taking much of the guesswork out of this design process, the framework could help engineers more readily create new adaptive materials while cutting development time and eliminating costs from failed prototypes. This framework could one day be used to design soft robotic grippers that respond automatically to their environment without any complex electronics, or morphing structures for airplane wings that predictably change their shape in response to temperature shifts.

“I’ve always been fascinated with natural materials and how complex behavior emerges from very simple building blocks,” says Lee Marom, an MIT graduate student in the departments of Mechanical Engineering and Architecture and lead author of a paper on this framework. “What really excites me about this work is going beyond bio-inspiration to what we could call ‘bio-derivation,’ where we move past observing a unique behavior to capturing the relationships and mechanisms that are actually producing that behavior, and then finding a systematic way to translate them into an engineered system.”

Marom is joined on the paper by corresponding author Markus Buehler, the Jerry McAfee Professor of Engineering in the departments of Civil and Environmental Engineering and Mechanical Engineering; Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, a principal investigator in the Laboratory for Information and Decision Systems, and an affiliate faculty with the Institute for Data, Systems, and Society; and Skylar Tibbits, an associate professor in the Department of Architecture. The research appears in the Journal of the Mechanics and Physics of Solids.

Biological building blocks

Pine cones can open and close their scales in response to humidity because of complex interactions within the organism’s structure. 

Shifts in humidity cause changes in microscopic cellulose fibers, which then cause transformations in larger groupings of fibers called laminas, which impact tissue layers, and so on, all the way up to the pinecone we see hanging from a tree branch.

“We instantiated the framework on the pine cone because it gives us a relatively simple, well-understood mechanism to demonstrate how the framework works. But its value becomes even greater as we apply it to more complex systems,” Marom says.

For engineers, the challenge is not necessarily reproducing an individual behavior, but translating the mechanisms and relationships that produce it across length scales. Without an explicit framework, these relationships need to be reformulated for each new system. 

To streamline the material design process, MIT researchers created a mathematical framework that captures how the components at each scale in a natural object work together to exhibit a certain behavior. The framework carries the design all the way to fabrication, translating the engineered behavior into verified manufacturing specifications and executable code that is used to 3D-print the object.

“What we were missing was a way to connect the mathematical description of a natural system all the way to its physical realization. The goal of this framework is to make that entire chain explicit so we can reason about what has to be preserved at each step,” Marom says.

The framework utilizes tools from category theory, which is a systematic method to compose larger systems from smaller ones in a way that is guaranteed to succeed.

Using category theory, the system maps out how a stimulus, such as humidity, causes a response at each level of the biological hierarchy within an organism like a pine cone. It models each level of the biological hierarchy as a separate building block that is independently validated.

Then the framework constructs a larger system from these building blocks by employing mathematical rules to ensure there is a valid transition between each step in the hierarchy. 

It assigns each building block in the natural system to a synthetic counterpart. In this way, the engineered material preserves the stimulus-response interactions that cause the natural organism’s unique behavior.

The work extends a research program in Buehler’s laboratory spanning more than a decade. 

Earlier studies used category theory to describe hierarchical materials and determine when building blocks could be replaced while preserving higher-level function. In subsequent work, Buehler and colleagues introduced “categorical prototyping,” using the same mathematics to preserve selected molecular-scale mechanics when translating computational models into large-scale 3D-printed prototypes. 

The new framework takes the next step by closing the entire chain, from multiscale biological mechanics, through an engineered realization and fabrication specification, to an experimentally validated, machine-executable design.

“Biological materials derive their extraordinary functionality from relationships that span scales, from molecular and fiber-level mechanisms to whole structures. Category theory gives us a way to make those relationships explicit and transferable. Once that design logic is captured mathematically, nature becomes a library of composable mechanisms that can be translated, recombined, and realized in new material systems,” Buehler says.

Compositional structure 

“Once we know that the relationships we mapped are valid, we can start recombining them in new ways. That means the framework isn’t only describing existing systems, it can also help us reason about ones we haven’t built before,” Marom explains.

For instance, the engineers mapped the humidity-driven bending behavior in a pine cone and the humidity-driven twisting behavior of a wheat awn as separate sets of building blocks. 

Then they combined some building blocks from each to design and fabricate a new type of actuator that exhibits thermal twisting behavior, without the need to do any new design work. When tested, the twisting actuator performed as the researchers expected.

In the future, engineers could use this framework to reliably combine verified components into new, bio-inspired designs for adaptive materials in applications like robotics, biomedical devices, or wearable technology.

“The systematization of our framework allows you to reuse pieces without needing to start from scratch each time, saving a huge amount of computation. That’s the real-world payoff,” Zardini says.

Now that the researchers have laid the groundwork with this mathematical framework, they can apply it to objects with more complex mechanics. They also plan to incorporate artificial intelligence models into their pipeline to expedite the discovery of new adaptive materials. 

“We have shown that the boundaries between disciplines do not matter as much as we think they do. Some of the principles from category theory can be used to guide and empower materials design. These mathematical structures seem to really have no boundaries,” Zardini says.

“The larger vision is physical AI: intelligence that can reason in terms of physical mechanisms and then turn those ideas into matter. Here we are beginning to build the infrastructure for that — composable physical knowledge, mathematical rules for determining what can be combined, and a path from a new design concept all the way to machine instructions and fabrication. Ultimately, this could allow AI not only to discover new materials and mechanisms, but to physically realize and test what it discovers,” Buehler says.  

This research was supported, in part, by the MIT Lemelson Engineering Fellowship, Singapore DSO National Laboratories, and the MIT Generative AI Impact Consortium.

MIT researchers have engineered bacteria that can function as transistors, allowing the team to create living “circuit boards” that can be printed onto a growth medium in a Petri dish. 

In electrical circuits, transistors function as switches that can turn current on or off. In the biological circuits that the researchers have created, bacterial switches control the flow of small molecules, which send signals to downstream circuit components. 

The research team designed two different transistors, along with three bacterial strains that relay information between the transistors, giving them the building blocks they need to design nearly any type of circuit. In a new study, they used these cells to create circuits that can add two or three inputs, or send one input to a specific location in the circuit.

“We’ve built some initial computer architecture components that are commonly used, but any operation can be built with these five strains,” says Hamid Doosthosseini PhD ’25, an MIT postdoc and the lead author of the new study. 

Using this approach, the researchers hope to develop circuits that one day could coat plant leaves or roots, where they could compute to sense and respond to environmental conditions such as drought or attack by pests. 

Christopher Voigt, head of MIT’s Department of Biological Engineering, is the senior author of the paper, which was recently published in Nature Chemical Biology. Former MIT postdoc Haorong Chen is also an author of the paper.

Cells as transistors

When designing synthetic biology circuits, researchers typically engineer cells to express proteins and transcription factors that interact to perform a task such as sensing a target molecule, which then triggers production of a specific output.

These simple circuits can perform various logic functions, but they must use unique transcription factors to avoid crosstalk within the circuit. There is a limited number of transcription factors that can be used for these circuits, which limits the overall complexity that can be achieved in a single cell. Additionally, putting too many circuits in one cell can overburden the cell’s protein production machinery.

In the new paper, the researchers took a different approach: Instead of building an entire circuit into one cell, they designed cells that could act as transistors. These transistors can then be combined in different ways to create a variety of circuits.

To create the transistors, the researchers chose a bacterium called Pantoea agglomerans, which commonly grows on surfaces, including plants. Using these cells, they made two types of transistors that can be switched on or off by a molecule called OC-6. One of the transistors is switched on by this input, and the other is switched off. Each transistor also detects the presence of a target molecule, in this case, OC-12. Depending on whether that molecule is present, and whether the switch is active, the transistors produce an output molecule known as OHC-14.

The researchers also used three strains of Pantoea agglomerans to create relays, which translate the OHC-14 signal into an output that can be fed into another transistor. Using these relay strains, the researchers can “wire” the transistors together, just like an electronic circuit board.

For example, they could create a bidirectional switch with two transistors that sense OC-12, and then send that information to different relay strains based on a switch input, ultimately feeding into other transistors that further process the signal.

The researchers created their circuits by printing colonies of bacteria onto plates containing agar, a growth medium. Each colony is printed about 5 millimeters from the nearest one. This allows the signals to travel only to the nearest colony, which then relays them to the next one, so information flows only in one direction.

Complex calculations

In this paper, the researchers demonstrated a transistor that can perform several types of logic operations depending on its location in the circuit layout, including “multi-input,” “or,” and “imply” gates. They also combined the transistors to create more complex circuits that can add up two signals, process more signals simultaneously, or function as a demultiplexer — a circuit that takes one incoming signal and sends it to one of several possible destinations, depending on a control signal.

The largest of these circuits, which adds two inputs together, contains 24 bacterial colonies wired together.

“This work shows that we can get toward more complicated functions by linking up simpler functions in individual cells,” Voigt says. “Computationally, there’s nothing that your iPhone can do that these circuits couldn’t do.” 

Circuits made from these cells take about eight hours to perform each calculation, much longer than a computer circuit. But, for biological applications, that is a reasonable amount of time, the researchers say. 

“We’re not trying to replace computers, but rather put computational control into biology. If you have bacteria on the root of a plant, or the plant itself is doing the computing, running a simple calculation overnight is fast enough relative to a growth season,” Voigt says.

If developed for use in agriculture, this type of circuit could be applied to the roots of plants to detect different types of stress. Once a particular input is detected, it would trigger a response such as synthesizing a fungicide.

The research was funded, in part, by the U.S. Defense Advanced Research Projects Agency and by the U.S. Intelligence Advanced Research Projects Activity.

Criminals are hacking into public Wi-Fi devices—at hotels, conference centers, and so on—around the world and changing their DNS settings. The goal is to redirect users to fake login pages and steal their credentials.

Washington state will impose a fire-resistant building code in high-risk areas. Wildfire-prone areas are being excluded, some say.

A POLITICO analysis finds that while the administration wiped out hundreds of billions in clean-energy tax breaks, spending has been harder to unwind.

New Jersey Rep. Frank Pallone said President Donald Trump's rejection of aid for his state is a "betrayal."

It looks at industry links by groups filing friend of the court briefs in the climate battle royale.

Skyrocketing demand for electricity could put too much stress on the system unless more transmission lines are built.

The push to reshape the utility liability system has become a keystone issue of Newsom’s final legislative session.

Agency employees have feared blowback from the president after their emails were sought by right-leaning operatives since he last left the White House.

To improve the chances of having a race in cooler weather, organizers are shifting race dates, starting earlier and even giving options for participants to cut their runs short.

Roughly 97 million people from the central Plains to the Southeast face heat advisories.

Mining has stripped away vast areas of rainforest, leaving behind a scarred landscape of contaminated pools and altered rivers.

As we move through everyday life, our brains engage in a huge variety of cognitive tasks. For example, during a grocery run, we might have to recall the items for a recipe, remember where the clerk said the flour was located, and count out money to pay.  

Scientists have long theorized that the brain contains modules, or clusters of neurons, that perform the same computation across many different types of tasks. This type of modularity could help explain why our brains are able to take on so many functions, with little difficulty.

In a new study of mice, MIT neuroscientists have found the first evidence for the existence of these flexible modules. They identified neurons in the prefrontal cortex that can be used to store either a sensory input or an action plan in working memory.

“We found that the brain doesn’t dedicate a separate group of neurons for every type of information. Instead, it uses the same populations of neurons to perform the same computation on different kinds of information, which means the same subset of neurons can hold both an action and a sensory stimulus in working memory,” says Yuma Osako, an MIT postdoc and the lead author of the new study.

The discovery supports the theory that reusable circuits allow the brain to mix and match components to generate a rich variety of behavior, the researchers say. 

Mriganka Sur, the Newton Professor of Neuroscience at MIT’s Picower Institute for Learning and Memory, and Timothy Buschman PhD ’08, a professor at the Princeton Neuroscience Institute, are the senior authors of the paper, which appears today in Nature Neuroscience. MIT graduate student Greggory Heller and postdoc Sofie Ahrlund-Richter are also authors of the study.

Cognitive building blocks

Dating back to his time as a graduate student at MIT, Buschman has been interested in understanding how the brain is able to perform so many different kinds of behavior. 

“One of the solutions that’s always been proposed has been this idea of compositionality — that you can take pieces of cognition that perform part of a task and reuse them in another task,” he says. 

In a study published last year, Buschman’s lab at Princeton showed that when animals perform a task such as categorizing objects based on their shape or color, they assemble neural circuits that perform different pieces of the task. Just like “cognitive Legos,” these building blocks can be flexibly combined to generate new behaviors.

Osako, who joined Sur’s lab several years ago, was also interested in studying cognitive flexibility. He and Sur teamed up with Buschman to explore a related question: whether individual neural circuits can be repurposed to perform different functions. 

“Our everyday life requires us to temporarily hold many different kinds of information. One big question is how the brain can represent an unlimited variability of information using only a finite number of neurons,” Osako says.

To get at that question, the researchers trained mice on a task in which they have to determine whether two sensory stimuli (high or low pitched tones) are the same, and respond accordingly. 

The researchers recorded electrical impulses from the brain while the mice performed this task, focusing on the prefrontal cortex, which is involved in executive functions such as planning and decision-making, and the parietal cortex, which processes sensory information and plans movement.

After measuring electrical activity from thousands of neurons, the researchers performed computational analyses that allowed them to identify groups of neurons that encode specific pieces of information.

They focused on two time periods — the time between the first and second tone, when the animals are holding a memory of the first tone, and the time between the second tone and the point where they have to decide on an action. During that second period, the animals are holding their decision and action plan in their working memory.

Within the parietal cortex, the researchers found that neurons appeared to exclusively store memory of the tone. But in the prefrontal cortex, they identified a cluster of neurons that could switch between the two types of memory. During the first period, they stored a memory of the first tone, but during the second, they were responsible for remembering the plan of action.

Re-using these clusters for different purposes allows the animals to flexibly store different types of information, the researchers say.

“When mice do tasks that test whether memory computations can be reused, the answer is they are. There are subspaces of functional activity in the prefrontal cortex that can be the substrate of mixing and matching toward flexible cognition,” Sur says.

Computational flexibility

The new findings offer support for the idea that the same computational circuits can be used for different purposes, Buschman says.

“The main result from this study is that there’s a circuit in the brain that maintains items in working memory, and you can put either sensory or motor information into it, and flexibly reuse it depending on what your current task is,” he says. “This means you do not have to build an entire new circuit for holding information in mind every time you want to learn a new task.”

The researchers now plan to study whether inhibiting these modules during different parts of the task affects the animals’ behavior, which could offer additional evidence that the flexible modules they identified participate in a variety of functions.

The research was funded by the National Institutes of Health, a MURI Grant, the Picower Institute Innovation Fund, the Japan Society for the Promotion of Science Overseas Research Fellowships, and the Uehara Memorial Foundation Postdoctoral Fellowship. 

Nature Climate Change, Published online: 17 August 2026; doi:10.1038/s41558-026-02712-y

The influence of fine particulate pollution (PM2.5) on plant water-use efficiency remains poorly understood. The authors highlight that PM2.5 exerts a primarily negative effect across scales due to asymmetric effects on photosynthesis and evapotranspiration.

Fascinating video about searching for life undersea. The video basically makes the point that our bright white searchlights are scaring everything away, and that red light is more neutral. That, plus bait to attract sea creatures, is teaching us a lot about what’s going on down there. Lots of footage of giant squid, and speculation about the colossal squid. Worth watching.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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