Author: Julie Pryor

Can (and should) consciousness be uploaded to a machine?

by Julie Pryor |

Consciousness transfer, the hypothetical process of moving a human mind to a digital medium, has been a cornerstone of science fiction movies and books for decades. But as AI advances, questions about whether human consciousness could be transferred to a machine have moved from science fiction to serious academic debate.

The human brain is widely considered the most complex structure in the known universe, with tens of billions of neurons and more than 100 trillion points of connection. The theoretical process of uploading the mind, or “whole brain emulation,” would require mapping all of these circuits and understanding their unique function – a feat that many neuroscientists say exceeds current technologies and computing power. Mapping even the poppy seed-sized brain of a fly required an unprecedented collaboration of hundreds of scientists, various AI tools, and a global community of human proofreaders six years to complete. Researchers estimate it will take another 10 to 15 years to do the same in the mouse.

These technical hurdles aside, consciousness transfer also raises profound philosophical questions about identity, mortality, and what it means to exist. Even if we could upload our minds to a machine, should we?

To explore these questions, we asked members of the McGovern community to weigh in on the debate.

Jeffrey Brown II

Jeffrey Brown II is an EECS PhD student in Edward Boyden’s lab at the McGovern Institute. He builds AI agents to help construct a complete and accurate wiring diagram of the brain.“

Working on this problem can be extremely stimulative for neuroscience because, if you approach it seriously, you will have to deal with the some of the field’s most challenging problems.” – Jeffrey Brown

Jeffrey Brown II is an EECS graduate student using AI tools in the Boyden lab to construct a complete wiring diagram of the brain.

No one one really knows how the brain works, says Brown, and it’s unclear how much we need to know about the human brain in order to build an AI model of it.

“We don’t even have a list of all the circuits in the human brain,” says Brown, adding that he hopes to scale this up as part of his doctoral thesis in the Boyden lab. “We know many pieces of the brain, but we don’t really know how they all interact across massive scales of space and time,” he explains.

Some researchers believe functional data suffices; others insist on biophysical accuracy; still others use different approaches entirely. There’s no scientific consensus on what “good enough” actually means, Brown says.

Brown emphasizes that serious work in this space could be tremendously valuable for neuroscience, pushing researchers to grapple with some of the field’s most fundamental challenges. “On the other hand, if you don’t do the science carefully, I think people could hurt themselves, most likely by ceding their health (physical and mental) or agency to building a digital model that is not really “them.””

Davy Deng

Davy Deng is a Harvard-MIT HST PhD student in Ed Boyden’s lab at the McGovern Institute. His research goal is to build a digital brain using high-resolution multimodal data from the nervous systems of small animals.

“The most scientifically grounded and promising path toward whole brain emulation involves studying the nervous system of a small worm.” – Davy Deng

Davy Deng is a Harvard-MIT HST PhD student in Ed Boyden’s developing technologies toward reconstruction of the mind in machines.

In his recent TEDxMIT talk, Deng highlights a striking geographic divide: “When I mention whole brain emulation in Silicon Valley, people lean forward… But if I mention this in Boston, I tend to get some suspicious looks.”

This hype and skepticism has clouded real progress, he says, and a rigorous scientific foundation is needed to make headway. He argues that C. elegans, a 1-millimeter transparent worm, offers a unique opportunity to do this. The complete wiring diagram of the worm is well established, yet scientists have been unsuccessful in emulating its brain activity in a computer.

“This tells us that a wiring diagram alone does not contain sufficient information to simulate a brain,” he says.

His solution is to incorporate two additional layers of information – the chemical signatures of each connection and whole-organism imaging during behavior – creating what he calls the “ground truth” necessary for brain emulation.

Ila Fiete

Ila Fiete is an associate investigator at the McGovern Institute, a professor of brain and cognitive sciences, and the director of the K. Lisa Yang ICoN Center at MIT.

“I do think consciousness transfer could be possible!” – Ila Fiete

McGovern Institute Associate Investigator Ila Fiete.

Initial proofs of concept based on the connectome show some promise, Fiete says. (Nature, Sept 2024; Nature, Oct 2024; NeurIPS 2025) These studies combine supervised learning with existing circuit knowledge to infer the function of individual neurons.

“The challenge will be to show whether it is possible to do this for the whole brain, where we do not have knowledge of what most of the circuits exactly do, or how they do it.”

Rather than declaring consciousness transfer inherently good or bad, Fiete recognizes that there are profound philosophical concerns that deserve careful attention from ethicists.

“I think once people have an idea, it’s impossible to stop it from being pursued,” she says. “That’s a pathology of our species stemming from our curiosity and drive for improvement.”

“Fortunately, I do think and hope that we are capable of building guardrails around how these things are done.”

Alan Jasanoff

Alan Jasanoff is an associate investigator at the McGovern Institute, the Eugene McDermott Professor in the Brain Sciences and Human Behavior at MIT. He is also the author of the book, “The Biological Mind: How Brain, Body, and Environment Collaborate to Make Us Who We Are.” 

“Given our current level of knowledge, it is hard to imagine how this could be done.
But never say never!” – Alan Jasanoff

Even if we mapped the billions of neurons and their interactions in the human brain, says Jasanoff, we still can’t easily explain the “phenomenal” aspect of consciousness: why we feel and experience things the way we do.

McGovern Institute Associate Investigator Alan Jasanoff.

“This aspect of consciousness doesn’t seem reducible to more fundamental characteristics the way that biological phenomena like disease, digestion, or even life itself seem to be,” he says.

“And if phenomenal consciousness were somehow recreated, consider what kind of existence awaits,” Jasanoff reflects.

“What would give that life satisfaction or a sense of purpose? The virtual consciousness might achieve freedom from biological suffering and death, but how could it escape virtual equivalents of those in the machine environment?”

Qiyao (Catherine) Liang

Catherine Liang is an EECS PhD student in Ila Fiete’s lab at the McGovern Institute. She studies how intelligence emerges in brains and artificial systems. 

“Consciousness uploading could be brilliant in principle, but terrible if developed or used without a clear understanding of its consequence.” – Catherine Liang

Catherine Liang is an EECS PhD student in the Fiete lab studying how intelligence emerges in biological and artificial systems.

“It may one day be possible to create models that imitate a person’s memories, personality, and behavior closely enough to function as a digital descendant,” says Liang. “Even whole-brain emulation may become a reality if consciousness depends mainly on the brain’s functional organization.”

“But that is assuming that consciousness is an emergent property of the physical brain, and precisely what level of details we need to emulate is the open question,” she says.

Consciousness uploading could theoretically overcome aging, disease, and biological limitations, says Liang, but it poses catastrophic risks if the process merely copies consciousness while the original person dies.

Profound ethical concerns would emerge around access, consent, ownership, and the legal rights of digital minds, she cautions.

Caitlin Lienkaemper

Caitlin Lienkaemper is the Swartz Foundation Postdoctoral Fellow for Theory in Neuroscience in Ila Fiete’s lab where she uses mathematical tools to study how the brain encodes and processes information.

“If I only existed as thoughts on a computer, I don’t think I’d be alive in a meaningful sense; I am my body as much as I am my thoughts.” – Caitlin Lienkaemper

Caitlin Lienkaemper is the Swartz Foundation Postdoctoral Fellow for Theory in Neuroscience in Ila Fiete’s lab.

While Lienkaemper acknowledges that a highly detailed simulation of a human brain could theoretically be conscious, she remains skeptical—in part because there’s no scientific consensus on how to measure consciousness in the first place.

“We don’t have a fully fleshed out theory of consciousness that would give us reason to trust any measurement,” she explains. “And even if we did, it’s unclear whether predictions valid for human brains would hold for machines.”

Beyond the technical barriers, though, Lienkaemper questions whether consciousness transfer is even desirable. She sees the pursuit as a “secular repackaging” of the concept of an immortal soul—and worries it fundamentally misses what makes life meaningful.

“I don’t think my thoughts are the only important thing about me,” she says. “I’m my body as much as I am my thoughts, and I value my connection to the natural world and to other people. Investing our energy in digital immortality rather than preserving Earth’s ecosystems seems both impractical and selfish.”

 

RareNet Symposium 2026

by Julie Pryor |

On June 9, the McGovern Institute convened leaders in science, biotechnology, and patient advocacy for RareNet 2026, a first-of-its-kind symposium aimed at dismantling the barriers between laboratory discovery and life-changing treatments for rare brain disorders.

Over 300 million people worldwide live with rare disorders—most affecting the brain and nervous system. Yet the vast majority lack an approved therapy. The Rare Brain Disorders Nexus (RareNet) was established at the McGovern Institute in 2025 by MIT alums Ana Méndez ’91 and Rajeev Jayavant ’86, (EE ’88, SM ’88) to address this need. Led by Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Neuroscience at MIT, RareNet draws together expertise from the MIT community and beyond to expedite the path from lab to clinic.

MIT President Sally Kornbluth set the tone at the inaugural RareNet symposium, thanking founders Méndez and Jayavant for helping MIT focus on this important challenge: “I look forward to watching RareNet dissolve needless barriers, accelerate timelines, and bring new hope to millions of patients and their families for whom hope is long overdue.”

MIT President Sally Kornbluth (third from left) with RareNet founders Rajeev Jayavant (far left) and Ana Méndez (center) at the June 9 symposium at the McGovern Institute. Also pictured are the founders’ son Neal (second from left), RareNet Director Guoping Feng, RareNet Scientific Advisor Xian Gao, and McGovern Institute Director Robert Desimone. Photo: Steph Stevens

RareNet’s collaborative vision came into focus at the symposium, where more than a dozen leading neuroscientists, biotech innovators, and patient advocates shared the podium. The scientific program spanned the full translational pathway, from fundamental discovery to clinical development.

Feng Zhang (McGovern Institute, MIT; HHMI; Broad Institute), Katherine High (RhyGaze AG; Rockefeller University), Kiran Musunuru (University of Pennsylvania), and Timothy Yu (Boston Children’s Hospital; Harvard Medical School) discussed emerging genetic medicines–including genome editing, gene therapy, and individualized therapeutic strategies–and the challenges involved in bringing them safely to patients.

Kevin Bender (University of California San Francisco), Christopher Walsh (Boston Children’s Hospital; Harvard Medical School), Sonia Vallabh (Broad Institute; MGH; Harvard Medical School), and Joseph Buxbaum (Icahn School of Medicine at Mount Sinai) explored how insights into disease mechanisms, human genetics, and patient-derived data are advancing research in neurodevelopmental disorders, autism, and prion disease.

Representatives from the Sturge-Weber and FOXP1 communities–including Karen Ball, Matt Shirley, and Samit Dasgupta–demonstrated how patient foundations can help define research priorities, build essential resources, and drive promising discoveries toward meaningful treatments.

Also among the day’s speakers was Monica Coenraads, who transformed her child’s rare disease into a powerful research initiative. When her daughter Chelsea was diagnosed with Rett syndrome in 1998 at age two, Coenraads faced an uncertain future. Today, as founder and CEO of the Rett Syndrome Research Trust, she is helping to rewrite that story for other families. In a compelling talk with John Sinnamon, Director of Research at RSRT, Coenraads shared both the scientific breakthroughs reshaping Rett syndrome treatment and the deeply personal journey that sparked it all.

“Monica and John’s talks capture the vital connection between patients and families, cutting-edge research, and translational innovation,” says RareNet Executive Director Xian Gao. “It’s a powerful reminder that behind every research breakthrough is a human story demanding progress.”

High-speed microscopy reveals electrical activity across the brain

by Anne Trafton |

Within the brain, neurons compute by generating electrical impulses. These signals travel throughout neurons, which are in turn connected in vast networks that control brain functions such as sensory perception, memory formation, and control of movement.

McGovern Institute Investigator Edward Boyden. Photo: Justin Knight

In an advance that could help neuroscientists map those neural networks, leading to a better understanding of how neural activity underlies behavior and other brain functions, MIT engineers have invented a new microscope that can image electrical activity in neurons distributed across the brain of an entire organism, the experimental model Danio rerio (zebrafish).

Using a microscope that they adapted for fast, high-volumetric rate imaging, the researchers were able to track electrical activity across the brain on the scale of milliseconds. This method revealed patterns of neural activity from neurons throughout the brain that were activated in response to ultraviolet light.

“All of the parts of the brain are connected together, so if you want to truly understand the brain, you have to understand how all the neurons work together as an emergent whole,” says Ed Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; and a member of MIT’s McGovern Institute for Brain Research, Yang Tan Collective, and the Koch Institute for Integrative Cancer Research.

Boyden is the senior author of the study, which appears today in Nature Methods. Former J. Douglas Tan Postdoctoral Fellow Zeguan Wang ’PhD 24 and former MIT research scientist Jie Zhang are the lead authors of the paper. Other authors include former MIT postdoc Panagiotis Symvoulidis, Picower Institute research scientist Wei Guo, graduate students Davy Deng and Lige Zhang, Koch Institute research scientist Adam Amsterdam, Picower Institute research scientist Takato Honda, Boston College undergraduate Steven Roche, and Matthew Wilson, the Sherman Fairchild Professor of Neuroscience at MIT and a member of the Picower Institute.

High-speed imaging

One technique often used to measure neuron activity in the brain is calcium imaging. Calcium flows into neurons after they fire an electrical impulse, so measuring calcium levels in the cells can serve as a proxy for neural activity. However, this type of imaging isn’t fast enough to capture single spikes of activity.

“Calcium imaging inherently is very slow, so you’re talking about imaging activity on the order of seconds or even minutes. Typically that is too slow for us to be able to see a lot of these high-speed neural activities,” Zhang says. “Neurons compute using electrical activity, so with voltage imaging, you can get direct observation of that.”

To enable direct imaging of voltage, researchers have developed proteins called genetically encoded voltage indicators — fluorescent proteins that can be genetically expressed in neurons. When a neuron fires an impulse, the protein fluoresces, which can be detected with a fluorescence microscope.

“All of the parts of the brain are connected together, so if you want to truly understand the brain, you have to understand how all the neurons work together as an emergent whole.” – Ed Boyden

In previous work, researchers have used these proteins to image small populations of neurons, usually focusing on one localized part of the brain. Until now, there hasn’t been a way to image a large volume, such as the entire brain, with the millisecond-scale resolution needed to see electrical impulses from individual neurons.

To achieve that, the MIT team decided to modify a commonly used microscope known as a light sheet microscope. This type of microscope uses a sheet of laser light to illuminate a thin slice of a sample. By imaging many layers in sequence, this technique can generate 3D images of a large volume. However, with previous microscopes, the scanning of an entire volume would take too long to be able to capture neuronal impulses across the volume at single cell resolution.

“Different groups of neurons that are distributed across the brain coordinate together at millisecond timescales to generate a lot of behaviors and brain computations,” Wang says. “To understand the principles, we need the technology to observe their activity at the same time, across the whole brain, so we are not missing any important participant neurons.”

To make the imaging process fast enough to image millisecond-scale activity, the researchers increased the image acquisition speed of the microscope’s camera, and they also boosted the scanning speed of the microscope using a technique called remote refocusing.

Using this approach, the researchers showed that they could scan the entire zebrafish brain 200 times per second, or once every five milliseconds.

Mapping brain activity

To test the new microscope, the researchers engineered neurons in larval zebrafish to express a voltage indicator called Positron2-Kv. Although they had hoped that the indicator would end up in every neuron, it produced signals in neurons distributed throughout the brain, with about one quarter of the neurons exhibiting acceptable signals. This was enough, however, to observe patterns of activity across the brain. The researchers imaged the brain as the fish were resting, and they were able to observe single voltage spikes from neurons, as well as rapid bursts of spikes.

Additionally, this technique revealed patterns in how the brain is activated following a stimulus such as ultraviolet light. Immediately following the stimulus, activity was seen in the optic tectum, which receives and processes visual input from the retina. This activity propagated from one side of a part of the brain called the tectum to the other. Stimulus-independent activity also occurred in sequences across sets of neurons in the cerebellum and hindbrain.

The new microscope scans the entire zebrafish brain 200 times per second, revealing brain-wide patterns of electrical activity. Each flashing spot represents an active neuron. Image: Zeguan Wang

The researchers now hope to increase the percentage of neurons that they can image across the brain, as well as the microscope’s speed and resolution. They are also working on expanding the use of this technique to other experimental models, including mice.

This approach, they say, could offer neuroscientists a new way to generate hypotheses about what happens in the brain when it engages in specific behaviors, or about how brain activity is linked to states of mind such as daydreaming.

“A big question is simply to understand how neurons work together as a network. And this might be the first time that you could do that, because you can image the voltage of neurons distributed throughout the network,” Boyden says.

The research was funded by the National Institutes of Health, the BRAIN Initiative, the Picower Institute Innovation Fund, K. Lisa Yang, Ashar Aziz, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience at MIT, the Hock E. Tan and K. Lisa Yang Center for Autism Research, the Alana Down Syndrome Center, John Doerr, Jed McCaleb, James Fickel, and the Howard Hughes Medical Institute.

 

 

DNA shaper steers nervous system development

by Jennifer Michalowski |

Portrait of Robert Horvitz at a computer.
McGovern Investigator Robert Horvitz shared the 2002 Nobel Prize in Medicine with colleagues Sydney Brenner and John Sulston for discoveries that helped explain how genes regulate programmed cell death and organ development. Photo: AP Images/Aynsley Floyd

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 findings, reported July 31, 2026, 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

Postdoctoral researcher 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.

Dongyeop Lee is a postdoctoral researcher in the Horvitz lab and the first author of the Science Advances paper.

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.”

How visual learning happens in the brain

by Jennifer Michalowski |

The wiring and rewiring of the brain never ends. Neural pathways are constantly being reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute and York University in Toronto, scientists are combining detailed analysis of brain activity with computational modeling to better understand that change.

McGovern Institute postdoctoral fellow Lynn Sörensen, Investigator James DiCarlo, and York University assistant professor Kohitij Kar worked together to compare what happened when monkeys and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes the team detected in the brains of monkeys.

Their work, reported today in the journal Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects. By modeling these changes, the researchers hope to better predict how training reshapes perception, which could one day inform educational strategies for a wide range of learners.

Subtle changes

Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed.

Neuroscientists have debated how much change occurs in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, but others have reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.  

To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented—so much so that it’s possible to “decode” what object a monkey is seeing and even predict what errors it’s likely to make in identifying it, simply by analyzing patterns of neural activity there.

The team recorded neural activity in the IT cortex from two groups of monkeys as the animals looked at and identified images of objects. Some of the monkeys were untrained, so the images they saw had little meaning to them. Others had already learned to identify similar objects, so they could usually discriminate between elephants, chairs, and other select objects, even when those objects were presented at different sizes, from different angles, or against different backgrounds than the ones they had seen before.

The broad pattern of activity in the IT cortex was largely similar in trained and untrained monkeys, suggesting that learning had not dramatically rewritten this high-level visual representation. Still, the group found subtle but reliable differences in the way neurons in the IT cortex responded to images in monkeys that had learned to recognize the kinds of objects they were shown, compared to the untrained monkeys.

Modeling learning

The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to monkey IT cortex to identify the same categories of objects the monkeys had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors.

Only some of the primate-like models showed learning behavior that matched that of the monkeys. In those that did, the IT-like stage changed in ways that resembled the learning-related changes the researchers had observed in the IT cortex of trained monkeys.

While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The researchers say the strong match in learning effects between the monkeys and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.

“This shows that you can actually build in silico versions of future experiments,” Sörensen says. “I think that gives us this playground of asking ‘what if’ questions—and potentially predicting new things that go beyond the experimenter’s intuition.”

Most of the changes that allowed for learning in the model occurred outside of the IT cortex. “This tells us that there is a lot between the area we recorded from and the final behavioral readout that needs to change during this process,” Kar says. He adds that the team’s model will be useful as researchers look more deeply into how downstream brain areas contribute to learning.

The researchers stress that their study allowed more granular measurements of brain activity than would be possible in humans, and because monkeys’ brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in monkeys’ IT cortex could help researchers design new learning strategies for humans.

“Our prior conceptual working model of you—or a monkey—learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system,” says DiCarlo, who is also the Peter de Florez Professor of Brain and Cognitive Sciences and Director of the MIT Siegel Family Quest for Intelligence. “You wouldn’t want your whole visual system to become an elephant detector [just because you’ve learned to identify an elephant]. But this study went beyond that to say actually, when you learn ‘elephant,’ your IT does change a little bit to make it a little more relevant to elephants.”

That likely has consequences for recognizing other visual features, too. Subtle changes in the IT cortex that support elephant recognition might also make you better at identifying things other than elephants, DiCarlo says. Likewise, the same changes might make it a little harder to identify something else.

These kinds of consequences may be difficult to predict intuitively, but become obvious with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.

 

 

Separating logic and language

by Jennifer Michalowski |

Portrait of a smiling scientist in a blue v-neck blouse.
Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Evelina Fedorenko’s lab. Photo by Caitlin Cunningham Photography.

Some people find it useful to talk through their problems—but language isn’t necessary for logical reasoning, cognitive neuroscientists at MIT’s McGovern Institute say. In research published today in the journal PNAS, researchers led by McGovern Institute investigator Evelina Fedorenko have shown that people can perform well on tasks that require logical reasoning even if their language abilities are severely impaired. What’s more, brain imaging shows that language-processing parts of the brain are not called on for logical reasoning. Fedorenko is also an associate professor of brain and cognitive sciences at MIT.

Philosophers, linguists, and cognitive scientists have debated the relationship between language and thought for thousands of years, with many arguing that we use language to think.  There are good reasons to suspect a close relationship between logic and language, acknowledges Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Fedorenko’s lab. “Abstract thinking has properties that look a lot like language,” she says, pointing to structural similarities: “You can decompose a thought into subcomponents, like little atoms of logical propositions, and you can combine them in a hierarchical manner to make more complex structured rules, very akin to language.”

But she and Fedorenko suspected that while we largely depend on language to communicate about logical reasoning—from presenting a problem to explaining how we have arrived at conclusions—the brain might use a separate system for the reasoning itself. “There are aspects of thinking that seem to go beyond some of the limitations of language,” Kean explains. Logical reasoning demands precision that language often lacks. And language is linear, progressing one word at a time, whereas evaluating available information to reach logical conclusions can require thinking in less linear ways.

Logical reasoning

These observations left Kean curious about how the brain handles logical reasoning. It’s a particularly difficult question to answer scientifically, because it’s hard to take language out of the equation when working with human study participants. But Fedorenko’s team did just that by collaborating with Rosemary Varley, a neuroscientist at University College London who studies acquired language disorders, and her team.

Together, the scientists worked with two patients who had experienced stroke that damaged language-processing parts of their brains, leaving them with severe impairments in both understanding and producing language. They designed language-free logic games in which participants were asked to infer relationships between sets of numbers. Given two lists, they had to figure out the hidden rule that turned one list into the other, such as reversing the digits or removing numbers above a certain value. Once they thought they’d discovered the rule, they had to apply it to new examples. In a second game, participants were presented a set of geometric patterns and asked to identify another pattern to complete the matrix.

As participants solved increasingly difficult puzzles, it became clear that people don’t need language for this kind of reasoning. Patients with language impairments solved the problems as well as a control group and were even able to communicate the rules they inferred using gestures or with a sketch. “It really upends a theory that says that symbolic rule induction is not possible without linguistic capacities,” says Kean.

Four brain scans with the bottom two scans showing activation areas in red.
Anatomical scans of two participants with severe aphasia (top row) and a projection of the probabilistic atlas for the language network into the MRI image of one of the participants. Image: Hope Kean

Alongside this part of the study, Kean and colleagues also used functional brain imaging to study what happens in the brains of healthy adults when they are engaged in logical reasoning. Participants in this part of the study visited MIT for a series of MRI scans, which captured images of their brain activity during an array of tasks. In addition to completing different kinds of logic games inside the scanner, participants were asked to engage in tasks designed to map the language-processing parts of their brain. Another set of tasks was used to map each person’s Multiple Demand network—a distributed brain system that supports complex problem solving.

These neurotypical participants completed logic games similar to those used with the language-impaired patients. They were also presented with problems that required syllogistic reasoning, using “if-then” statements such as “If the ball is red, then it is big. The ball is red. Is the ball big?” The team varied the difficulty of the logic puzzles so they could see which brain areas became more active when the need for logical reasoning intensified. Likewise, they looked for changes in brain activity when participants had to infer a hidden rule versus simply applying a rule they’d been given.

Here too, a separation between language and logic was clear: The MRI scans showed the brain’s language system is not engaged for either inductive reasoning (when participants identified hidden rules) or deductive reasoning (when they assessed the validity of syllogistic conclusions). Surprisingly, the Multiple Demand network, which many scientists had suspected was important for logical reasoning, was engaged during inductive reasoning but didn’t seem to get involved in deductive reasoning—a finding Kean is building on in her ongoing work.

For Fedorenko and Kean, the findings are strong support for a separation of logic and language in the brain. They add to previous findings from Fedorenko’s lab showing that other types of thinking, such as object categorization and social reasoning, also do not rely on language.

Acquired language impairments and AI

The researchers say these findings have important implications for how we think about acquired language impairments, or aphasia. Specialists who work with people with aphasia have long recognized that loss of language does not mean loss of intelligence. People with aphasia can continue to enjoy playing chess, solving sudoku puzzles, or being in charge of the family’s finances. But it is common for others to confuse their communicative difficulties with thinking difficulties.

“This research adds to a growing body of work establishing that even severely aphasic individuals can preserve their ability for abstract logical thought—a defining feature of our species,” Fedorenko says. “We should continue to educate the public that linguistic difficulties—in aphasia, but also in those with developmental language conditions, such as stuttering, or those who do not speak English natively—are not indicative of how smart or capable someone is.”

There could be implications for artificial intelligence, too. Large language models like ChatGPT and Claude are trained entirely on text and use text as their output—yet they convincingly simulate some kinds of human reasoning. Exploring the differences between these models and the human brain, where language and abstract logical thought are distinct, might offer useful insights to inform future models, Kean says.

When it comes to understanding how the human brain reasons, Kean calls this a new frontier in the geography of thought—and she says it’s one she is eager to explore.

 

The brain’s language network is more extensive than previously thought

Anne Trafton |

For decades, neuroscientists have known that specific regions in the brain’s left hemisphere are responsible for processing language. However, a new study from MIT shows that language processing also occurs in many other parts of the brain.

Using functional magnetic resonance imaging (fMRI) data from more than 700 people, the researchers identified 17 additional regions of the brain that appear to play a role in language. These regions are scattered across the brain, including parts of the cerebellum, hippocampus, and cerebral cortex, and they make up about 5 percent of the total volume of the adult brain — about the size of a large strawberry.

“Even though there are all these distant components, it’s pretty restricted in terms of volume. You don’t need that much of the brain to do language,” says Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Exactly how these regions contribute to language processing is still to be discovered, although the researchers have made some progress toward determining the functions of the cerebellar regions that they identified.

A group of smiling researchers stand in front of a wall of windows.
The authors of the Journal of Neuroscience manuscript pictured together, from left to right: Benjamin Lipkin, Colton Casto, Evelilna Fedorenko, Agata Wolna, Aaron Wright, and Sam Hutchinson. Photo: Alexandra Sokhina

MIT postdoc Agata Wolna is the lead author of the paper, which appears today in the Journal of Neuroscience. Other authors include Aaron Wright, a K. Lisa Yang Post-Baccalaureate Research Scholar at MIT; Colton Casto, a graduate student at Harvard University; Samuel Hutchinson, a graduate student at MIT; and Benjamin Lipkin PhD ’26.

Tracking language

The brain’s language processing centers include Broca’s area, first discovered in the 1800s, plus additional regions in the left frontal and temporal lobes of the brain. Scientists have found that some of the corresponding areas of the right hemisphere also contribute to processing language, especially the social-emotional components of language.

There have also been hints that other parts of the brain might be involved in language processing. Early in her career, Fedorenko’s language studies often showed active brain regions outside of the canonical language centers, but she says she was discouraged from including them in her papers.

“When we initially started looking at language, in the first couple of papers, I tried to be comprehensive and include anything that seemed consistent across participants, and there was a huge amount of resistance,” she says.

“People would say things like, ‘Well, we know those are not language areas, so please focus on the language areas.’”

In the new study, she and Wolna wanted to revisit those brain scans and see if they could systematically identify language regions outside of the standard language-processing areas.

To do that, they analyzed data from 772 people who had been scanned in Fedorenko’s lab since 2013. Each of these participants underwent a task known as a language localizer, which is used to determine the location of language processing areas for each subject.

During the test, participants read or listen to sentences as well as sequences of nonwords. For each person, the researchers measure the difference in strength of response when reading real sentences or nonsense sequences. The brain areas that work harder during the sentence condition are considered to be doing something relevant to language, especially if they respond while both reading and listening to sentences.

“It’s a very simple paradigm that lets you identify this core language system in individual brains,” Wolna says.

When searching for language areas, the researchers usually use a relatively strict statistical threshold. In this study, they relaxed the threshold and also used some targeted searches in subcortical areas, in hopes of finding all areas that may contribute to language processing. “We always see this frontal temporal network, but there’s quite a lot of evidence that there are other regions that are also critical for language processing,” Wolna says. “By using a laxer threshold and zooming in on areas with weak MRI signal, we tried to maximize the chances of finding small and weakly responsive regions outside of this left frontal temporal system.”

A widespread network

For about 490 of the participants, the researchers also had data on how their brain responded during a spatial working memory task — remembering the locations of flashing squares on a grid. This task engages a brain network called the multiple demand system, which does not overlap with the core language areas.

This task allowed the researchers to ask whether any of the newly identified language-sensitive regions specifically respond to language and not more general cognitive processes.

Of the 17 new language sites that were revealed by this study, five are located in the cerebellum, which is mainly involved in coordinating the body’s movement. In a study published earlier this year, researchers led by Casto found that three of those cerebellar regions also became engaged during some nonlinguistic cognitive tasks, which was also seen in the new study.

“Those areas that respond to both language and some other tasks could be really interesting and important because they may be doing something like integrating information from different cortical systems,” Fedorenko says.

They also found language-selective regions in the medial frontal cortex, the bottom surface of the left temporal lobe, the hippocampus, and the amygdala. The researchers now plan to further study how these brain regions might contribute to language processing.

A graphic illustrating the established language regions of the brain (red) alongside the new, extended language regions discovered by Fedorenko’s team (blue). Image: Agata Wolna

“We can now test some ideas from past work, and also more rigorously characterize these regions across different kinds of language manipulations, and different kinds of non-linguistic tasks, to try to understand what it is that they’re doing,” Fedorenko says.

The research was funded by the Simons Center for the Social Brain at MIT, the McGovern Institute, MIT’s Department of Brain and Cognitive Sciences, and the MIT Siegel Family Quest for Intelligence.

 

 

Two McGovern faculty appointed to named professorships at MIT

by Julie Pryor |

MIT has appointed McGovern faculty members Sven Dorkenwald and Josh McDermott to named professorships that will provide additional support for their “outstanding research and educational careers.” Named professorships at MIT are prestigious endowed faculty chairs that provide crucial financial support for both junior faculty and senior scholars, enabling them to pursue bold research and global challenges.

Dorkenwald, who recently joined MIT as an assistant professor of brain and cognitive sciences and an investigator at the McGovern Institute, has been selected to hold the  Silverman (1968) Family Career Development Professorship for a three-year term beginning July 1, 2026. A trailblazer in the field of computational neuroscience, Dorkenwald reconstructs maps of neuronal circuits to investigate how they support complex computations. He is recognized for his leadership in connectomics—an emerging discipline focused on reconstructing and analyzing neural circuitry at unprecedented scale and detail. Jeffrey Silverman ’68 is a life member emeritus of the MIT Corporation. His generous gift to the institute empowers early career professors to pursue high-risk research. 

McDermott, a professor of brain and cognitive sciences and an associate investigator at the McGovern Institute, has been selected to hold the Uncas (1923) and Helen Whitaker Professorship for a five-year renewable term beginning July 1, 2026. McDermott’s research operates at the intersection of psychology, neuroscience, and engineering to study how people hear and interpret sound. Groundbreaking discoveries from the McDermott lab are informing new treatments for hearing loss, and paving the way for machine systems that emulate the human ability to recognize and interpret sound. The Uncas (1923) and Helen Whitaker Professorship chair was established in 1980 through a gift from the late Helen Whitaker, the first woman elected to life membership of the MIT Corporation. It is designed to support distinguished faculty whose work spans multiple disciplines to solve complex, real-world problems. 

 

The brain’s internal ruler

by Jennifer Michalowski |

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

If you are crossing an unfamiliar room in the dark, you may grope around a bit to get a sense of your space.

But for many animals, feeling out a space comes more naturally. A mouse, for instance, can efficiently navigate in the dark just by grazing its whiskers against walls and other obstacles.

Fan Wang, a professor of brain and cognitive sciences and an investigator at the McGovern Institute, has discovered how neurons in a mouse’s brainstem use signals from the animal’s touch-sensitive whiskers to estimate an object’s distance from the face.

Her team’s findings, published online June 25, 2026, in the journal Neuron, unlock key circuitry the brain uses to represent the space immediately surrounding the body.

Mapping space

The circuit the team discovered is part of the brain’s system for creating an egocentric map of space—that is, understanding where things are relative to one’s own body. Neuroscientists know that the brain calls on specialized circuits to understand space in this way, which are different from its system for mapping space using external landmarks.

In their study, Wang and her team explored how the brain maps the space closest to the body, which is known as the peripersonal space. This is the space in which we move, and it is vital that we understand where things are in relationship to our bodies so we can reach, step, avoid hazards, and otherwise interact effectively with our environment.

Wang says mice were an appealing model for investigating how the brain understands objects’ distance within the peripersonal space, because a rodent’s whiskers seem so much like a built-in set of rulers. These whiskers, which vary in length, are swept back and forth as the animals explore their environment. As whiskers bend and vibrate, the mechanical sensations are relayed to the brain by sensory neurons at their base. Those neurons fire more when a whisker bends close to the face than they do in response to contact near the whisker’s tip, communicating information about the proximity of the touch.

Close-up image of a mouse peeking through a hole with whiskers grazing the edge of the hole. Image: Istockphoto

Wang’s team wanted to know if the brain uses these signals to build an internal ruler-like representation of distance more precise than “near” or “far.” To find out, graduate student Wenxi Xiao and research scientist Kyle Severson monitored neural activity in a small sensory-processing region in the brainstem where tactile signals from the whiskers first arrive in the brain. They studied what happened there as mice walked on a treadmill while brushing their whiskers against a wall that passed by at different distances.

Many neurons in the region were sensitive to the whisker bending triggered by the wall. Some behaved similarly to the sensory neurons they were getting their information from, firing more when the wall was closer to the face and thus serving as a proximity-based distance code. But other cells were tuned in to discrete distances, firing only when the distance of the wall the whiskers had touched was within a specific range.

For some neurons, activity peaked when the wall was 23 mm away from the face, near the tips of the longest whiskers. Others responded most when the wall was at intermediate distances.

“Each of these neurons represents a specific distance, and together they span the full range reached by the longest whisker, like tick marks on the ruler,” Wang explains. “We call that the map code.”

The team wanted to know how the brain converts proximity signals from different whiskers into accurate map code of object’s distances from the head. “You cannot just listen to individual whisker neurons, because a contact at the tip of a short whisker would be in the middle of a long whisker. You need a brain circuit to build a unified distance map,” Wang says.

Through computational modeling and by exploring what happened when they manipulated neural signaling in specific ways, Wang’s team showed how distances can be calculated by comparing inputs from different sensory neurons. Their findings suggest that each brainstem neuron that makes up the map code receives both direct excitatory inputs from proximity-sensitive whisker neurons and inhibitory inputs from neurons driven by proximity-dependent whisker touch signals.

“Essentially the inhibitory pathway allows the brainstem to compare two inputs by subtraction,” Wang explains. “If one input signals ‘this is how far it is’ and the other signals ‘this is how far I estimate it to be,’ subtracting one from the other yields an intermediate value. We think it’s a simple and elegant way to transform tactile input into a representation of discrete distance.”

Wang notes that despite their importance, the brain’s body-centered representations of space have so far received little attention from neuroscientists, who know much more about how we understand locations in space relative to landmarks (an allocentric map). She is eager to investigate how the egocentric map code her team discovered is integrated with other brain systems to guide movement, social interactions, and other behavior, and hopes the findings will further exploration from other groups.

The study was funded by grants from the National Institutes of Health.

Would you return a favor? Scientists say it depends on the relationship

by Anne Trafton |

When a friend buys you a cup of coffee, it’s likely that next time, you’ll return the gesture. This type of reciprocal generosity has been well-documented in behavioral economic studies.

However, anthropologists and other social scientists have known for decades that in the context of relationships where one person has more power, status, or influence, reciprocal generosity is usually not the norm.

Researchers at MIT have now experimentally demonstrated, for the first time, that small changes to the relationship context can dramatically change people’s actions and expectations of reciprocal generosity.

During interactions between people of different social status, people tend to expect that generosity will flow one way, and it can be either up or down. It may be that a professor always buys coffee for her students, or that a student always offers to help carry groceries for his resident advisor. Once the precedent is established, it is expected to continue.

One interpretation of the findings is that keeping track of whose turn it is to do a favor is the exception in social interactions, not the rule. That is, it is extra work that we do when we want to maintain equal relationships.

“In many intimate relationships, hierarchical relationships, or other kinds of role-based relationships, you don’t put in the work of trying to keep track of turns,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and associate dean of science at MIT. “Under this interpretation, we just follow precedent because following a precedent is easier. We all know what to expect, and we don’t have to keep track of what happened last time.”

Saxe is the senior author of the study, which appears in the journal Open Mind. MIT graduate student Alicia Chen is the paper’s lead author.

Changing expectations

Most experimental studies of generosity have been done in the context of behavioral economics and game theory. In such experiments, people are usually paired with a stranger and asked to play games that require coordination. Such studies have found that people tend to use turn-taking and reciprocity as their default strategies. These scenarios, however, are stripped from any social context that might exist between people in the real world.

Saxe and Chen wanted to see if they could measure the effects of social context by incorporating relationships into the type of experiments used to evaluate people’s expectations regarding generosity.

“Where generosity becomes hard and complicated is when it starts to occur in the context of existing relationships, because it changes the terms of the relationships,” Saxe says. “What’s expected of you is very different within a relationship than outside of one.”

To study these effects, the researchers designed experiments in which participants read stories about different types of interactions. In some of the scenarios, the subjects of the stories were described as having either symmetric or asymmetric relationships. In others, they were given specific social relationships such as aunt-niece or manager-employee.

Each story described interactions that might be seen in typical daily life, such as buying coffee for a co-worker or preparing a meal for one’s family. Participants were then asked to predict what would happen the next time the interaction occurred.

In all of these scenarios, the researchers found that people expected that generous acts would be reciprocated when they occurred between individuals in symmetric relationships such as friends, cousins, or co-workers of equal rank. However, their expectations changed for asymmetric relationships, where each person has a different social status. In those cases, people expected that any precedent that was set would continue in the future.

One possible explanation for this is that reciprocity is not the norm but an exception that only occurs in the interactions between equals or strangers, the researchers say. Many of our interactions are with people with whom we have asymmetric relationship, and to maintain those relationships, it’s simply easier to follow precedent.

“If there’s no need to keep track of our equal status, then in some ways it’s the default to fall back on following precedents,” Saxe says.

Maintaining relationships

The study showed that in asymmetric relationships, generosity could flow in either direction. Once that direction was established, it was expected to continue. For example, after an older brother bought concert tickets for a much younger brother, the study participants expected that the older brother would also buy the tickets for the next concert.

“We found that when people know the relationship is asymmetric, they don’t expect reciprocity; they expect the same action to keep on going,” Chen says. “If the lower-rank person acts generously, people expect that to continue, and if the higher-rank person acts generously, people expect that to continue.”

Following precedents is not only easier, but keeping up these actions may help solidify and define existing relationships. For example, anthropologists have long known that gift-giving helps to construct and maintain social relationships.

“Following a precedent can be a way of actively maintaining relationships and hierarchies, when the asymmetry of the exchange truly reflects the asymmetry of the relationship,” Saxe says.

The researchers are now working on creating computational models that could be used to analyze different factors that people take into account when they’re considering whether someone might reciprocate a generous act. In addition to the factors examined in this study, others could include how much each person will benefit, what type of relationship they’re in, and culturally specific expectations of how people should act in different situations.

“One really powerful thing about these models is that we can build in existing theories, add things to the models, and then compare how much these extra factors, like considerations related to social relationships, matter in terms of explaining what people are doing,” Chen says. “This allows us to quantitatively compare the different theories to each other.”

The research was funded by the Simons Foundation Autism Research Initiative and the Patrick J. McGovern Foundation.