Category: Uncategorized

How one brain circuit encodes memories of both places and events

Anne Trafton |

Nearly 50 years ago, neuroscientists discovered cells within the brain’s hippocampus that store memories of specific locations. These cells also play an important role in storing memories of events, known as episodic memories. While the mechanism of how place cells encode spatial memory has been well-characterized, it has remained a puzzle how they encode episodic memories.

A new model developed by MIT researchers explains how those place cells can be recruited to form episodic memories, even when there’s no spatial component. According to this model, place cells, along with grid cells found in the entorhinal cortex, act as a scaffold that can be used to anchor memories as a linked series.

“This model is a first-draft model of the entorhinal-hippocampal episodic memory circuit. It’s a foundation to build on to understand the nature of episodic memory. That’s the thing I’m really excited about,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

The model accurately replicates several features of biological memory systems, including the large storage capacity, gradual degradation of older memories, and the ability of people who compete in memory competitions to store enormous amounts of information in “memory palaces.”

MIT Research Scientist Sarthak Chandra and Sugandha Sharma PhD ’24 are the lead authors of the study, which appears today in Nature. Rishidev Chaudhuri, an assistant professor at the University of California at Davis, is also an author of the paper.

An index of memories

To encode spatial memory, place cells in the hippocampus work closely with grid cells — a special type of neuron that fires at many different locations, arranged geometrically in a regular pattern of repeating triangles. Together, a population of grid cells forms a lattice of triangles representing a physical space.

In addition to helping us recall places where we’ve been, these hippocampal-entorhinal circuits also help us navigate new locations. From human patients, it’s known that these circuits are also critical for forming episodic memories, which might have a spatial component but mainly consist of events, such as how you celebrated your last birthday or what you had for lunch yesterday.

“The same hippocampal and entorhinal circuits are used not just for spatial memory, but also for general episodic memory,” says Fiete, who is also the director of the K. Lisa Yang ICoN Center at MIT. “The question you can ask is what is the connection between spatial and episodic memory that makes them live in the same circuit?”

Two hypotheses have been proposed to account for this overlap in function. One is that the circuit is specialized to store spatial memories because those types of memories — remembering where food was located or where predators were seen — are important to survival. Under this hypothesis, this circuit encodes episodic memories as a byproduct of spatial memory.

An alternative hypothesis suggests that the circuit is specialized to store episodic memories, but also encodes spatial memory because location is one aspect of many episodic memories.

In this work, Fiete and her colleagues proposed a third option: that the peculiar tiling structure of grid cells and their interactions with hippocampus are equally important for both types of memory — episodic and spatial. To develop their new model, they built on computational models that her lab has been developing over the past decade, which mimic how grid cells encode spatial information.

“We reached the point where I felt like we understood on some level the mechanisms of the grid cell circuit, so it felt like the time to try to understand the interactions between the grid cells and the larger circuit that includes the hippocampus,” Fiete says.

In the new model, the researchers hypothesized that grid cells interacting with hippocampal cells can act as a scaffold for storing either spatial or episodic memory. Each activation pattern within the grid defines a “well,” and these wells are spaced out at regular intervals. The wells don’t store the content of a specific memory, but each one acts as a pointer to a specific memory, which is stored in the synapses between the hippocampus and the sensory cortex.

When the memory is triggered later from fragmentary pieces, grid and hippocampal cell interactions drive the circuit state into the nearest well, and the state at the bottom of the well connects to the appropriate part of the sensory cortex to fill in the details of the memory. The sensory cortex is much larger than the hippocampus and can store vast amounts of memory.

“Conceptually, we can think about the hippocampus as a pointer network. It’s like an index that can be pattern-completed from a partial input, and that index then points toward sensory cortex, where those inputs were experienced in the first place,” Fiete says. “The scaffold doesn’t contain the content, it only contains this index of abstract scaffold states.”

Furthermore, events that occur in sequence can be linked together: Each well in the grid cell-hippocampal network efficiently stores the information that is needed to activate the next well, allowing memories to be recalled in the right order.

Modeling memory cliffs and palaces

The researchers’ new model replicates several memory-related phenomena much more accurately than existing models that are based on Hopfield networks — a type of neural network that can store and recall patterns.

While Hopfield networks offer insight into how memories can be formed by strengthening connections between neurons, they don’t perfectly model how biological memory works. In Hopfield models, every memory is recalled in perfect detail until capacity is reached. At that point, no new memories can form, and worse, attempting to add more memories erases all prior ones. This “memory cliff” doesn’t accurately mimic what happens in the biological brain, which tends to gradually forget the details of older memories while new ones are continually added.

The new MIT model captures findings from decades of recordings of grid and hippocampal cells in rodents made as the animals explore and forage in various environments. It also helps to explain the underlying mechanisms for a memorization strategy known as a memory palace. One of the tasks in memory competitions is to memorize the shuffled sequence of cards in one or several card decks. They usually do this by assigning each card to a particular spot in a memory palace — a memory of a childhood home or other environment they know well. When they need to recall the cards, they mentally stroll through the house, visualizing each card in its spot as they go along. Counterintuitively, adding the memory burden of associating cards with locations makes recall stronger and more reliable.

The MIT team’s computational model was able to perform such tasks very well, suggesting that memory palaces take advantage of the memory circuit’s own strategy of associating inputs with a scaffold in the hippocampus, but one level down: Long-acquired memories reconstructed in the larger sensory cortex can now be pressed into service as a scaffold for new memories. This allows for the storage and recall of many more items in a sequence than would otherwise be possible.

The researchers now plan to build on their model to explore how episodic memories could become converted to cortical “semantic” memory, or the memory of facts dissociated from the specific context in which they were acquired (for example, Paris is the capital of France), how episodes are defined, and how brain-like memory models could be integrated into modern machine learning.

The research was funded by the U.S. Office of Naval Research, the National Science Foundation under the Robust Intelligence program, the ARO-MURI award, the Simons Foundation, and the K. Lisa Yang ICoN Center.

Scientists engineer CRISPR enzymes that evade the immune system

by Allessandra DiCorato | Broad Institute |

The core components of CRISPR-based genome-editing therapies are bacterial proteins called nucleases that can stimulate unwanted immune responses in people, increasing the chances of side effects and making these therapies potentially less effective.

Researchers at the Broad Institute of MIT and Harvard and Cyrus Biotechnology have now engineered two CRISPR nucleases, Cas9 and Cas12, to mask them from the immune system. The team identified protein sequences on each nuclease that trigger the immune system and used computational modeling to design new versions that evade immune recognition. The engineered enzymes had similar gene-editing efficiency and reduced immune responses compared to standard nucleases in mice.

Appearing today in Nature Communications, the findings could help pave the way for safer, more efficient gene therapies. The study was led by Feng Zhang, a core institute member at the Broad and an Investigator at the McGovern Institute for Brain Research at MIT.

“As CRISPR therapies enter the clinic, there is a growing need to ensure that these tools are as safe as possible, and this work tackles one aspect of that challenge,” said Zhang, who is also a co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics, the James and Patricia Poitras Professor of Neuroscience, and a professor at MIT. He is an Investigator at the Howard Hughes Medical Institute.

Rumya Raghavan, a graduate student in Zhang’s lab when the study began, and Mirco Julian Friedrich, a postdoctoral scholar in Zhang’s lab, were co-first authors on the study.

“People have known for a while that Cas9 causes an immune response, but we wanted to pinpoint which parts of the protein were being recognized by the immune system and then engineer the proteins to get rid of those parts while retaining its function,” said Raghavan.

“Our goal was to use this information to create not only a safer therapy, but one that is potentially even more effective because it is not being eliminated by the immune system before it can do its job,” added Friedrich.

In search of immune triggers

Many CRISPR-based therapies use nucleases derived from bacteria. About 80 percent of people have pre-existing immunity to these proteins through everyday exposure to these bacteria, but scientists didn’t know which parts of the nucleases the immune system recognized.

To find out, Zhang’s team used a specialized type of mass spectrometry to identify and analyze the Cas9 and Cas 12 protein fragments recognized by immune cells. For each of two nucleases — Cas9 from Streptococcus pyogenes and Cas12 from Staphylococcus aureus — they identified three short sequences, about eight amino acids long, that evoked an immune response. They then partnered with Cyrus Biotechnology, a company co-founded by University of Washington biochemist David Baker that develops structure-based computational tools to design proteins that evade the immune response. After Zhang’s team identified immunogenic sequences in Cas9 and Cas12, Cyrus used these computational approaches to design versions of the nucleases that did not include the immune-triggering sequences.

Zhang’s lab used prediction software to validate that the new nucleases were less likely to trigger immune responses. Next, the team engineered a panel of new nucleases informed by these predictions and tested the most promising candidates in human cells and in mice that were genetically modified to bear key components of the human immune system. In both cases, they found that the engineered enzymes resulted in significantly reduced immune responses compared to the original nucleases, but still cut DNA at the same efficiency.

Minimally immunogenic nucleases are just one part of safer gene therapies, Zhang’s team says. In the future, they hope their methods may also help scientists design delivery vehicles to evade the immune system.

This study was funded in part by the Poitras Center for Psychiatric Disorders Research, the K. Lisa. Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience and the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT.

Feng Zhang awarded 2024 National Medal of Technology

by Julie Pryor |

This post is adapted from an MIT News story.

***

Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and an Investigator at the McGovern Institute, has won the National Medal of Technology and Innovation, the nation’s highest recognition for scientists and engineers. The prestigious award recognizes “American innovators whose vision, intellect, creativity, and determination have strengthened America’s economy and improved our quality of life.”

Zhang, who is also a professor of brain and cognitive sciences and biological engineering at MIT, a core member of the Broad Institute of MIT and Harvard, and an investigator with the Howard Hughes Medical Institute, was recognized for his work developing molecular tools, including the CRISPR genome-editing system, that have accelerated biomedical research and led to the first FDA-approved gene editing therapy.

This year, the White House awarded the National Medal of Science to 14 recipients and named nine individual awardees of the National Medal of Technology and Innovation, along with two organizations. Zhang is among four MIT faculty members who were awarded the nation’s highest honors for exemplary achievement and leadership in science and technology.

Designing molecular tools

Zhang, who earned his undergraduate degree from Harvard University in 2004, has contributed to the development of multiple molecular tools to accelerate the understanding of human disease. While a graduate student at Stanford University, from which he received his PhD in 2009, Zhang worked in the lab of Professor Karl Deisseroth. There, he worked on a protein called channelrhodopsin, which he and Deisseroth believed held potential for engineering mammalian cells to respond to light.

The resulting technique, known as optogenetics, is now used widely used in neuroscience and other fields. By engineering neurons to express light-sensitive proteins such as channelrhodopsin, researchers can either stimulate or silence the cells’ electrical impulses by shining different wavelengths of light on them. This has allowed for detailed study of the roles of specific populations of neurons in the brain, and the mapping of neural circuits that control a variety of behaviors.

In 2011, about a month after joining the MIT faculty, Zhang attended a talk by Harvard Medical School Professor Michael Gilmore, who studies the pathogenic bacterium Enteroccocus. The scientist mentioned that these bacteria protect themselves from viruses with DNA-cutting enzymes known as nucleases, which are part of a defense system known as CRISPR.

“I had no idea what CRISPR was, but I was interested in nucleases,” Zhang told MIT News in 2016. “I went to look up CRISPR, and that’s when I realized you might be able to engineer it for use for genome editing.”

In January 2013, Zhang and members of his lab reported that they had successfully used CRISPR to edit genes in mammalian cells. The CRISPR system includes a nuclease called Cas9, which can be directed to cut a specific genetic target by RNA molecules known as guide strands.

Since then, scientists in fields from medicine to plant biology have used CRISPR to study gene function and modify faulty genes that cause disease. More recently, Zhang’s lab has devised many enhancements to the original CRISPR system, such as making the targeting more precise and preventing unintended cuts in the wrong locations. In 2023, the FDA approved Casgevy, a CRISPR gene therapy based on Zhang’s discoveries, for the treatment of sickle cell disease and beta thalassemia.

The National Medal of Technology and Innovation was established in 1980 and is administered for the White House by the U.S. Department of Commerce’s Patent and Trademark Office. The award recognizes those who have made lasting contributions to America’s competitiveness and quality of life and helped strengthen the nation’s technological workforce.

How the brain prevents us from falling

by MIT Dept of Brain and Cognitive Sciences |

This post is adapted from an MIT research news story.

***

As we navigate the world, we adapt our movement in response to changes in the environment. From rocky terrain to moving escalators, we seamlessly modify our movements to maximize energy efficiency and our reduce risk of falling. The computational principles underlying this phenomenon, however, are not well understood.

In a recent paper published in the journal Nature Communications, MIT researchers proposed a model that explains how humans continuously adapt yet remain stable during complex tasks like walking.

“Much of our prior theoretical understanding of adaptation has been limited to episodic tasks, such as reaching for an object in a novel environment,” says senior author Nidhi Seethapathi, the Frederick A. (1971) and Carole J. Middleton Career Development Assistant Professor of Brain and Cognitive Sciences at MIT. “This new theoretical model captures adaptation phenomena in continuous long-horizon tasks in multiple locomotor settings.”

Barrett Clark, a robotics software engineer at Bright Minds Inc and and Manoj Srinivasan, an associate professor in the Department of Mechanical and Aerospace Engineering at Ohio State University, are also authors on the paper.

Principles of locomotor adaptation

In episodic tasks, like reaching for an object, errors during one episode do not affect the next episode. In tasks like locomotion, errors can have a cascade of short-term and long-term consequences to stability unless they are controlled. This makes the challenge of adapting locomotion in a new environment  more complex.

To build the model, the researchers identified general principles of locomotor adaptation across a variety of task settings, and  developed a unified modular and hierarchical model of locomotor adaptation, with each component having its own unique mathematical structure.

The resulting model successfully encapsulates how humans adapt their walking in novel settings such as on a split-belt treadmill with each foot at a different speed, wearing asymmetric leg weights, and wearing  an exoskeleton. The authors report that the model successfully reproduced human locomotor adaptation phenomena across novel settings in 10 prior studies and correctly predicted the adaptation behavior observed in two new experiments conducted as part of the study.

The model has potential applications in sensorimotor learning, rehabilitation, and wearable robotics.

“Having a model that can predict how a person will adapt to a new environment has immense utility for engineering better rehabilitation paradigms and wearable robot control,” says Seethapathi, who is also an associate investigator at MIT’s McGovern Institute. “You can think of a wearable robot itself as a new environment for the person to move in, and our model can be used to predict how a person will adapt for different robot settings. Understanding such human-robot adaptation is currently an experimentally intensive process, and our model  could help speed up the process by narrowing the search space.”

For healthy hearing, timing matters

by Jennifer Michalowski |

When soundwaves reach the inner ear, neurons there pick up the vibrations and alert the brain. Encoded in their signals is a wealth of information that enables us to follow conversations, recognize familiar voices, appreciate music, and quickly locate a ringing phone or crying baby.

Seated man, smiling at camera
McGovern Institute Associate Investigator Josh McDermott. Photo: Justin Knight

Neurons send signals by emitting spikes—brief changes in voltage that propagate along nerve fibers, also known as action potentials. Remarkably, auditory neurons can fire hundreds of spikes per second, and time their spikes with exquisite precision to match the oscillations of incoming soundwaves.

With powerful new models of human hearing, scientists at MIT’s McGovern Institute have determined that this precise timing is vital for some of the most important ways we make sense of auditory information, including recognizing voices and localizing sounds.

The findings, reported December 4, 2024, in the journal Nature Communications, show how machine learning can help neuroscientists understand how the brain uses auditory information in the real world. McGovern Investigator Josh McDermott, who led the research, explains that his team’s models better equip researchers to study the consequences of different types of hearing impairment and devise more effective interventions.

Science of sound

The nervous system’s auditory signals are timed so precisely, researchers have long suspected that timing is important to our perception of sound. Soundwaves oscillate at rates that determine their pitch: low-pitched sounds travel in slow waves, whereas high-pitched sound waves oscillate more frequently. The auditory nerve that relays information from sound-detecting hair cells in the ear to the brain generates electrical spikes that corresponds to the frequency of these oscillations. “The action potentials in an auditory nerve get fired at very particular points in time relative to the peaks in the stimulus waveform,” explains McDermott, who is also an associate professor of brain and cognitive sciences at MIT.

This relationship, known as phase-locking, requires neurons to time their spikes with sub-millisecond precision. But scientists haven’t really known how informative these temporal patterns are to the brain. Beyond being scientifically intriguing, McDermott says, the question has important clinical implications: “If you want to design a prosthesis that provides electrical signals to the brain to reproduce the function of the ear, it’s arguably pretty important to know what kinds of information in the normal ear actually matter,” he says.

This has been difficult to study experimentally: Animal models can’t offer much insight into how the human brain extracts structure in language or music, and the auditory nerve is inaccessible for study in humans. So McDermott and graduate student Mark Saddler turned to artificial neural networks.

Artificial hearing

Neuroscientists have long used computational models to explore how sensory information might be decoded by the brain, but until recent advances in computing power and machine learning methods, these models were limited to simulating simple tasks. “One of the problems with these prior models is that they’re often way too good,” says Saddler, who is now at the Technical University of Denmark. For example, a computational model tasked with identifying the higher pitch in a pair of simple tones is likely to perform better than people who are asked to do the same thing. “This is not the kind of task that we do every day in hearing,” Saddler points out. “The brain is not optimized to solve this very artificial task.” This mismatch limited the insights that could be drawn from this prior generation of models.

To better understand the brain, Saddler and McDermott wanted to challenge a hearing model to do things that people use their hearing for in the real world, like recognizing words and voices. That meant developing an artificial neural network to simulate the parts of the brain that receive input from the ear. The network was given input from some 32,000 simulated sound-detecting sensory neurons and then optimized for various real-world tasks.

The researchers showed that their model replicated human hearing well—better than any previous model of auditory behavior, McDermott says. In one test, the artificial neural network was asked to recognize words and voices within dozens of types of background noise, from the hum of an airplane cabin to enthusiastic applause. Under every condition, the model performed very similarly to humans.

“The ability to link patterns of firing in the auditory nerve with behavior opens a lot of doors.” – Josh McDermott

When the team degraded the timing of the spikes in the simulated ear, however, their model could no longer match humans’ ability to recognize voices or identify the locations of sounds. For example, while McDermott’s team had previously shown that people use pitch to help them identify people’s voices, the model revealed that that this ability is lost without precisely timed signals. “You need quite precise spike timing in order to both account for human behavior and to perform well on the task,” Saddler says. That suggests that the brain uses precisely timed auditory signals because they aid these practical aspects of hearing.

The team’s findings demonstrate how artificial neural networks can help neuroscientists understand how the information extracted by the ear influences our perception of the world, both when hearing is intact and when it is impaired. “The ability to link patterns of firing in the auditory nerve with behavior opens a lot of doors,” McDermott says.

“Now that we have these models that link neural responses in the ear to auditory behavior, we can ask, ‘If we simulate different types of hearing loss, what effect is that going to have on our auditory abilities?’” McDermott says. “That will help us better diagnose hearing loss, and we think there are also extensions of that to help us design better hearing aids or cochlear implants.” For example, he says, “The cochlear implant is limited in various ways—it can do some things and not others. What’s the best way to set up that cochlear implant to enable you to mediate behaviors? You can, in principle, use the models to tell you that.”

Personal interests can influence how children’s brains respond to language

by Rubina Veerakone |

A new study from the McGovern Institute shows how interests can modulate language processing in children’s brains and paves the way for personalized brain research.

The paper, which appears in Imaging Neuroscience, was conducted in the lab of McGovern Institute Investigator John Gabrieli, and led by senior author Anila D’Mello, a former McGovern postdoctoral fellow and current assistant professor at the University of Texas Southwestern Medical Center and the University of Texas at Dallas.

“Traditional studies give subjects identical stimuli to avoid confounding the results,” says Gabrieli, who is also the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT.

“However, our research tailored stimuli to each child’s interest, eliciting stronger—and more consistent—activity patterns in the brain’s language regions across individuals.” – John Gabrieli

Funded by the Hock E. Tan and K. Lisa Yang Center for Autism Research in MIT’s Yang Tan Collective, this work unveils a new paradigm that challenges current methods and shows how personalization can be a powerful strategy in neuroscience. The paper’s co-first authors are Halie Olson, a postdoctoral associate at the McGovern Institute, and Kristina Johnson, an assistant professor at Northeastern University and former doctoral student at the MIT Media Lab. “Our research integrates participants’ lived experiences into the study design,” says Johnson. “This approach not only enhances the validity of our findings but also captures the diversity of individual perspectives, often overlooked in traditional research.”

Taking interest into account

When it comes to language, our interests are like operators behind the switchboard. They guide what we talk about and who we talk to. Research suggests that interests are also potent motivators and can help improve language skills. For instance, children score higher on reading tests when the material covers topics that are interesting to them.

But neuroscience has shied away from using personal interests to study the brain, especially in the realm of language. This is mainly because interests, which vary between people, could throw a wrench into experimental control—a core principle that drives scientists to limit factors that can muddle the results.

Gabrieli, D’Mello, Olson, and Johnson ventured into this unexplored territory. The team wondered if tailoring language stimuli to children’s interests might lead to higher responses in language regions of the brain. “Our study is unique in its approach to control the kind of brain activity our experiments yield, rather than control the stimuli we give subjects,” says D’Mello. “This stands in stark contrast to most neuroimaging studies that control the stimuli but might introduce differences in each subject’s level of interest in the material.”

Three women posing for photo with brain images in background.
Researchers Halie Olson (left), Kristina Johnson (center), and Anila D’Mello (right). Photo: Caitlin Cunningham

In their recent study, the authors recruited a cohort of 20 children to investigate how personal interests affected the way the brain processes language. Caregivers described their child’s interests to the researchers, spanning baseball, train lines, Minecraft, and musicals. During the study, children listened to audio stories tuned to their unique interests. They were also presented with audio stories about nature (this was not an interest among the children) for comparison. To capture brain activity patterns, the team used functional magnetic resonance imaging (fMRI), which measures changes in blood flow caused by underlying neural activity.

New insights into the brain

“We found that, when children listened to stories about topics they were really interested in, they showed stronger neural responses in language areas than when they listened to generic stories that weren’t tailored to their interests,” says Olson. “Not only does this tell us how interests affect the brain, but it also shows that personalizing our experimental stimuli can have a profound impact on neuroimaging results.”

The researchers noticed a particularly striking result. “Even though the children listened to completely different stories, their brain activation patterns were more overlapping with their peers when they listened to idiosyncratic stories compared to when they listened to the same generic stories about nature,” says D’Mello. This, she notes, points to how interests can boost both the magnitude and consistency of signals in language regions across subjects without changing how these areas communicate with each other.

 

Individual activation maps from three participants showing increased engagement of language regions for personally interesting versus generic narratives. Image courtesy of the researchers.

Gabrieli noted another finding: “In addition to the stronger engagement of language regions for content of interest, there was also stronger activation in brain regions associated with reward and also with self-reflection.” Personal interests are individually relevant and can be rewarding, potentially driving higher activation in these regions during personalized stories.

These personalized paradigms might be particularly well-suited to studies of the brain in unique or neurodivergent populations. Indeed, the team is already applying these methods to study language in the brains of autistic children.

This study breaks new ground in neuroscience and serves as a prototype for future work that personalizes research to unearth further knowledge of the brain. In doing so, scientists can compile a more complete understanding of the type of information that is processed by specific brain circuits and more fully grasp complex functions such as language.

3 Questions: Claire Wang on training the brain for memory sports

by Zach Winn | MIT News |

On Nov. 10, some of the country’s top memorizers converged on MIT’s Kresge Auditorium to compete in a “Tournament of Memory Champions” in front of a live audience.

The competition was split into four events: long-term memory, words-to-remember, auditory memory, and double-deck of cards, in which competitors must memorize the exact order of two decks of cards. In between the events, MIT faculty who are experts in the science of memory provided short talks and demos about memory and how to improve it. Among the competitors was MIT’s own Claire Wang, a sophomore majoring in electrical engineering and computer science. Wang has competed in memory sports for years, a hobby that has taken her around the world to learn from some of the best memorists on the planet. At the tournament, she tied for first place in the words-to-remember competition.

The event commemorated the 25th anniversary of the USA Memory Championship Organization (USAMC). USAMC sponsored the event in partnership with MIT’s McGovern Institute for Brain Research, the Department of Brain and Cognitive Sciences, the MIT Quest for Intelligence, and the company Lumosity.

MIT News sat down with Wang to learn more about her experience with memory competitions — and see if she had any advice for those of us with less-than-amazing memory skills.

Q: How did you come to get involved in memory competitions?

A: When I was in middle school, I read the book “Moonwalking with Einstein,” which is about a journalist’s journey from average memory to being named memory champion in 2006. My parents were also obsessed with this TV show where people were memorizing decks of cards and performing other feats of memory. I had already known about the concept of “memory palaces,” so I was inspired to explore memory sports. Somehow, I convinced my parents to let me take a gap year after seventh grade, and I travelled the world going to competitions and learning from memory grandmasters. I got to know the community in that time and I got to build my memory system, which was really fun. I did a lot less of those competitions after that year and some subsequent competitions with the USA memory competition, but it’s still fun to have this ability.

Q: What was the Tournament of Memory Champions like?

A: USAMC invited a lot of winners from previous years to compete, which was really cool. It was nice seeing a lot of people I haven’t seen in years. I didn’t compete in every event because I was too busy to do the long-term memory, which takes you two weeks of memorization work. But it was a really cool experience. I helped a bit with the brainstorming beforehand because I know one of the professors running it. We thought about how to give the talks and structure the event.

Then I competed in the words event, which is when they give you 300 words over 15 minutes, and the competitors have to recall each one in order in a round robin competition. You got two strikes. A lot of other competitions just make you write the words down. The round robin makes it more fun for people to watch. I tied with someone else — I made a dumb mistake — so I was kind of sad in hindsight, but being tied for first is still great.

Since I hadn’t done this in a while (and I was coming back from a trip where I didn’t get much sleep), I was a bit nervous that my brain wouldn’t be able to remember anything, and I was pleasantly surprised I didn’t just blank on stage. Also, since I hadn’t done this in a while, a lot of my loci and memory palaces were forgotten, so I had to speed-review them before the competition. The words event doesn’t get easier over time — it’s just 300 random words (which could range from “disappointment” to “chair”) and you just have to remember the order.

Q: What is your approach to improving memory?

A: The whole idea is that we memorize images, feelings, and emotions much better than numbers or random words. The way it works in practice is we make an ordered set of locations in a “memory palace.” The palace could be anything. It could be a campus or a classroom or a part of a room, but you imagine yourself walking through this space, so there’s a specific order to it, and in every location I place certain information. This is information related to what I’m trying to remember. I have pictures I associate with words and I have specific images I correlate with numbers. Once you have a correlated image system, all you need to remember is a story, and then when you recall, you translate that back to the original information.

Doing memory sports really helps you with visualization, and being able to visualize things faster and better helps you remember things better. You start remembering with spaced repetition that you can talk yourself through. Allowing things to have an emotional connection is also important, because you remember emotions better. Doing memory competitions made me want to study neuroscience and computer science at MIT.

The specific memory sports techniques are not as useful in everyday life as you’d think, because a lot of the information we learn is more operative and requires intuitive understanding, but I do think they help in some ways. First, sometimes you have to initially remember things before you can develop a strong intuition later. Also, since I have to get really good at telling a lot of stories over time, I have gotten great at visualization and manipulating objects in my mind, which helps a lot.

Season’s Greetings from the McGovern Institute

by Julie Pryor |

For this year’s holiday greeting, we asked the McGovern Institute community what comes to mind when they think of the winter holidays. More than 100 words were submitted for the project. The words were fed into ChatGPT to generate our holiday “prediction.” And a text-to-music generator (Udio) converted the words into a holiday song.

With special thanks to Jarrod Hicks and Jamal Williams from the McDermott lab for the inspiration…and to AI for pushing the boundaries of science and imagination.

Video credits:
Jacob Pryor (animation)
JR Narrows, Space Lute (sound design)

Revisiting reinforcement learning

by Jennifer Michalowski |

MIT Institute Professor Ann Graybiel. Photo: Justin Knight

Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in a number of psychiatric conditions, from mood disorders to addiction. Now, researchers led by Ann Graybiel, an investigator at MIT’s McGovern Institute, have found surprising patterns of dopamine signaling that suggest neuroscientists may need to refine their model of how reinforcement learning occurs in the brain. The team’s findings were published October 14, 2024, in the journal Nature Communications.

Dopamine plays a critical role in teaching people and other animals about the cues and behaviors that portend both positive and negative outcomes; the classic example of this type of learning is the dog that Ivan Pavlov trained to anticipate food at the sound of bell. Graybiel explains that according to the standard model of reinforcement learning, when an animal is exposed to a cue paired with a reward, dopamine-producing cells initially fire in response to the reward. As animals learn the association between the cue and the reward, the timing of dopamine release shifts, so it becomes associated with the cue instead of the reward itself.

But with new tools enabling more detailed analyses of when and where dopamine is released in the brain, Graybiel’s team is finding that this model doesn’t completely hold up. The group started picking up clues that the field’s model of reinforcement learning was incomplete more than ten years ago, when Mark Howe, a graduate student in the lab, noticed that the dopamine signals associated with reward were released not in a sudden burst the moment a reward was obtained, but instead before that, building gradually as a rat got closer to its treat. Dopamine might actually be communicating to the rest of the brain the proximity of the reward, they reasoned. “That didn’t fit at all with the standard, canonical model,” Graybiel says.

Dopamine dynamics

As other neuroscientists considered how a model of reinforcement learning could take those findings into account, Graybiel and postdoctoral researcher Min Jung Kim decided it was time to take a closer look at dopamine dynamics.

“We thought, let’s go back to the most basic kind of experiment and start all over again,” Graybiel says.

That meant using sensitive new dopamine sensors to track the neurotransmitter’s release in the brains of mice as they learned to associated a blue light with a satisfying sip of water. The team focused its attention on the striatum, a region within the brain’s basal ganglia, where neurons use dopamine to influence neural circuits involved in a variety of processes, including reward-based learning.

The researchers found that the timing of dopamine release varied in different parts of the striatum. But nowhere did Graybiel’s team find a transition in dopamine release timing from the time of the reward to the time to the cue—the key transition predicted by the standard model of reinforcement learning model.

In the team’s simplest experiments, where every time a mouse saw a light it was paired with a reward, the lateral part of the striatum reliably released dopamine when animals were given their water. This strong response to the reward never diminished, even as the mice learned to expect the reward when they saw a light. In the medial part of the striatum, in contrast, dopamine was never released at the time of the reward. Cells there always fired when a mouse saw the light, even early in the learning process. This was puzzling, Graybiel says, because at the beginning of learning, dopamine would have been predicted to respond to the reward itself.

The patterns of dopamine release became even more unexpected when Graybiel’s team introduced a second light into its experimental setup. The new light, in a different position than the first, did not signal a reward. Mice watched as either light was given as the cue, one at a time, with water accompanying only the original cue.

In these experiments, when the mice saw the reward-associated light, dopamine release went up in the centromedial striatum and surprisingly, stayed up until the reward was delivered. In the lateral part of the region, dopamine also involved a sustained period where signaling plateaued.

Graybiel says she was surprised to see how much dopamine responses changed when the experimenters introduce the second light. The responses to the rewarded light were different when the other light could be shown in other trials, even though the mice saw only one light at a time. “There must be a cognitive aspect to this that comes into play,” she says. “The brain wants to hold onto the information that the cue has come on for a while.” Cells in the striatum seem to achieve this through the sustained dopamine release that continued during the brief delay between the light and the reward in the team’s experiments. Indeed, Graybiel said, while this kind of sustained dopamine release has not previously been linked to reinforcement learning, it is reminiscent of sustained signaling that has been tied to working memory in other parts of the brain.

Reinforcement learning, reconsidered

Ultimately, Graybiel says, “many of our results didn’t fit reinforcement learning models as traditionally—and by now canonically—considered.” That suggests neuroscientists’ understanding of this process will need to evolve as part of the field’s deepening understanding of the brain. “But this is just one step to help us all refine our understanding and to have reformulations of the models of how basal ganglia influence movement and thought and emotion. These reformulations will have to include surprises about the reinforcement learning system vis-á-vis these plateaus, but they could possibly give us insight into how a single experience can linger in this reinforcement-related part of our brains,” she says.

This study was funded by the National Institutes of Health, the William N. & Bernice E. Bumpus Foundation, the Saks Kavanaugh Foundation, the CHDI Foundation, Joan and Jim Schattinger, and Lisa Yang.

Four from MIT named 2025 Rhodes Scholars

Anne Trafton |

Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo have been selected as 2025 Rhodes Scholars and will begin fully funded postgraduate studies at Oxford University in the U.K. next fall. In addition to MIT’s two U.S. Rhodes winners, Ouigbo and Nair, two affiliates were awarded international Rhodes Scholarships: Chen for Rhodes’ China constituency and Hector for the Global Rhodes Scholarship. Hector is the first Haitian citizen to be named a Rhodes Scholar.

The scholars were supported by Associate Dean Kim Benard and the Distinguished Fellowships team in Career Advising and Professional Development. They received additional mentorship and guidance from the Presidential Committee on Distinguished Fellowships.

“It is profoundly inspiring to work with our amazing students, who have accomplished so much at MIT and, at the same time, thought deeply about how they can have an impact in solving the world’s major challenges,” says Professor Nancy Kanwisher who co-chairs the committee along with Professor Tom Levenson. “These students have worked hard to develop and articulate their vision and to learn to communicate it to others with passion, clarity, and confidence. We are thrilled but not surprised to see so many of them recognized this year as finalists and as winners.

Yiming Chen ’24

Yiming Chen, from Beijing, China, and the Washington area, was named one of four Rhodes China Scholars on Sept 28. At Oxford, she will pursue graduate studies in engineering science, working toward her ongoing goal of advancing AI safety and reliability in clinical workflows.

Chen graduated from MIT in 2024 with a BS in mathematics and computer science and an MEng in computer science. She worked on several projects involving machine learning for health care, and focused her master’s research on medical imaging in the Medical Vision Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Collaborating with IBM Research, Chen developed a neural framework for clinical-grade lumen segmentation in intravascular ultrasound and presented her findings at the MICCAI Machine Learning in Medical Imaging conference. Additionally, she worked at Cleanlab, an MIT-founded startup, creating an open-source library to ensure the integrity of image datasets used in vision tasks.

Chen was a teaching assistant in the MIT math and electrical engineering and computer science departments, and received a teaching excellence award. She taught high school students at the Hampshire College Summer Studies in Math and was selected to participate in MISTI Global Teaching Labs in Italy.

Having studied the guzheng, a traditional Chinese instrument, since age 4, Chen served as president of the MIT Chinese Music Ensemble, explored Eastern and Western music synergies with the MIT Chamber Music Society, and performed at the United Nations. On campus, she was also active with Asymptones a capella, MIT Ring Committee, Ribotones, Figure Skating Club, and the Undergraduate Association Innovation Committee.

Wilhem Hector

Wilhem Hector, a senior from Port-au-Prince, Haiti, majoring in mechanical engineering, was awarded a Global Rhodes Scholarship on Nov 1. The first Haitian national to be named a Rhodes Scholar, Hector will pursue at Oxford a master’s in energy systems followed by a master’s in education, focusing on digital and social change. His long-term goals are twofold: pioneering Haiti’s renewable energy infrastructure and expanding hands-on opportunities in the country‘s national curriculum.

Hector developed his passion for energy through his research in the MIT Howland Lab, where he investigated the uncertainty of wind power production during active yaw control. He also helped launch the MIT Renewable Energy Clinic through his work on the sources of opposition to energy projects in the U.S. Beyond his research, Hector had notable contributions as an intern at Radia Inc. and DTU Wind Energy Systems, where he helped develop computational wind farm modeling and simulation techniques.

Outside of MIT, he leads the Hector Foundation, a nonprofit providing educational opportunities to young people in Haiti. He has raised over $80,000 in the past five years to finance their initiatives, including the construction of Project Manus, Haiti’s first open-use engineering makerspace. Hector’s service endeavors have been supported by the MIT PKG Center, which awarded him the Davis Peace Prize, the PKG Fellowship for Social Impact, and the PKG Award for Public Service.

Hector co-chairs both the Student Events Board and the Class of 2025 Senior Ball Committee and has served as the social chair for Chocolate City and the African Students Association.

Anushka Nair

Anushka Nair, from Portland, Oregon, will graduate next spring with BS and MEng degrees in computer science and engineering with concentrations in economics and AI. She plans to pursue a DPhil in social data science at the Oxford Internet Institute. Nair aims to develop ethical AI technologies that address pressing societal challenges, beginning with combating misinformation.

For her master’s thesis under Professor David Rand, Nair is developing LLM-powered fact-checking tools to detect nuanced misinformation beyond human or automated capabilities. She also researches human-AI co-reasoning at the MIT Center for Collective Intelligence with Professor Thomas Malone. Previously, she conducted research on autonomous vehicle navigation at Stanford’s AI and Robotics Lab, energy microgrid load balancing at MIT’s Institute for Data, Systems, and Society, and worked with Professor Esther Duflo in economics.

Nair interned in the Executive Office of the Secretary General at the United Nations, where she integrated technology solutions and assisted with launching the High-Level Advisory Body on AI. She also interned in Tesla’s energy sector, contributing to Autobidder, an energy trading tool, and led the launch of a platform for monitoring distributed energy resources and renewable power plants. Her work has earned her recognition as a Social and Ethical Responsibilities of Computing Scholar and a U.S. Presidential Scholar.

Nair has served as President of the MIT Society of Women Engineers and MIT and Harvard Women in AI, spearheading outreach programs to mentor young women in STEM fields. She also served as president of MIT Honors Societies Eta Kappa Nu and Tau Beta Pi.

David Oluigbo

David Oluigbo, from Washington, is a senior majoring in artificial intelligence and decision making and minoring in brain and cognitive sciences. At Oxford, he will undertake an MSc in applied digital health followed by an MSc in modeling for global health. Afterward, Oluigbo plans to attend medical school with the goal of becoming a physician-scientist who researches and applies AI to address medical challenges in low-income countries.

Since his first year at MIT, Oluigbo has conducted neural and brain research with Ev Fedorenko at the McGovern Institute for Brain Research and with Susanna Mierau’s Synapse and Network Development Group at Brigham and Women’s Hospital. His work with Mierau led to several publications and a poster presentation at the Federation of European Societies annual meeting.

In a summer internship at the National Institutes of Health Clinical Center, Oluigbo designed and trained machine-learning models on CT scans for automatic detection of neuroendocrine tumors, leading to first authorship on an International Society for Optics and Photonics conference proceeding paper, which he presented at the 2024 annual meeting. Oluigbo also did a summer internship with the Anyscale Learning for All Laboratory at the MIT Computer Science and Artificial Intelligence Laboratory.

Oluigbo is an EMT and systems administrator officer with MIT-EMS. He is a consultant for Code for Good, a representative on the MIT Schwarzman College of Computing Undergraduate Advisory Group, and holds executive roles with the Undergraduate Association, the MIT Brain and Cognitive Society, and the MIT Running Club.