Researcher: Ila Fiete

Approaching human cognition from many angles

by Alli Armijo | MIT News correspondent |

In January, as the Charles River was starting to freeze over, Keith Murray and the other members of MIT’s men’s heavyweight crew team took to erging on the indoor rowing machine. For 80 minutes at a time, Murray endured one of the most grueling workouts of his college experience. To distract himself from the pain, he would talk with his teammates, covering everything from great philosophical ideas to personal coffee preferences.

For Murray, virtually any conversation is an opportunity to explore how people think and why they think in certain ways. Currently a senior double majoring in computation and cognition, and linguistics and philosophy, Murray tries to understand the human experience based on knowledge from all of these fields.

“I’m trying to blend different approaches together to understand the complexities of human cognition,” he says. “For example, from a physiological perspective, the brain is just billions of neurons firing all at once, but this hardly scratches the surface of cognition.”

Murray grew up in Corydon, Indiana, where he attended the Indiana Academy for Science, Mathematics, and Humanities during his junior year of high school. He was exposed to philosophy there, learning the ideas of Plato, Socrates, and Thomas Aquinas, to name a few. When looking at colleges, Murray became interested in MIT because he wanted to learn about human thought processes from different perspectives. “Coming to MIT, I knew I wanted to do something philosophical. But I wanted to also be on the more technical side of things,” he says.

Once on campus, Murray immediately pursued an opportunity through the Undergraduate Research Opportunity Program (UROP) in the Digital Humanities Lab. There he worked with language-processing technology to analyze gendered language in various novels, with the end goal of displaying the data for an online audience. He learned about the basic mathematical models used for analyzing and presenting data online, to study the social implications of linguistic phrases and expressions.

Murray also joined the Concourse learning community, which brought together different perspectives from the humanities, sciences, and math in a weekly seminar. “I was exposed to some excellent examples of how to do interdisciplinary work,” he recalls.

In the summer before his sophomore year, Murray took a position as a researcher in the Harnett Lab, where instead of working with novels, he was working with mice. Alongside postdoc Lucas Fisher, Murray trained mice to do navigational tasks using virtual reality equipment. His goal was to explore neural encoding in navigation, understanding why the mice behaved in certain ways after being shown certain stimuli on the screens. Spending time in the lab, Murray became increasingly interested in neuroscience and the biological components behind human thought processes.

He sought out other neuroscience-related research experiences, which led him to explore a SuperUROP project in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). Working under Professor Nancy Lynch, he designed theoretical models of the retina using machine learning. Murray was excited to apply the techniques he learned in 9.40 (Introduction to Neural Computation) to address complex neurological problems. Murray considers this one of his most challenging research experiences, as the experience was entirely online.

“It was during the pandemic, so I had to learn a lot on my own; I couldn’t exactly do research in a lab. It was a big challenge, but at the end, I learned a lot and ended up getting a publication out of it,” he reflects.

This past semester, Murray has worked in the lab of Professor Ila Fiete in the McGovern Institute for Brain Research, constructing deep-learning models of animals performing navigational tasks. Through this UROP, which builds on his final project from Fiete’s class 9.49 (Neural Circuits for Cognition), Murray has been working to incorporate existing theoretical models of the hippocampus to investigate the intersection between artificial intelligence and neuroscience.

Reflecting on his varied research experiences, Murray says they have shown him new ways to explore the human brain from multiple perspectives, something he finds helpful as he tries to understand the complexity of human behavior.

Outside of his academic pursuits, Murray has continued to row with the crew team, where he walked on his first year. He sees rowing as a way to build up his strength, both physically and mentally. “When I’m doing my class work or I’m thinking about projects, I am using the same mental toughness that I developed during rowing,” he says. “That’s something I learned at MIT, to cultivate the dedication you put toward something. It’s all the same mental toughness whether you apply it to physical activities like rowing, or research projects.”

Looking ahead, Murray hopes to pursue a PhD in neuroscience, looking to find ways to incorporate his love of philosophy and human thought into his cognitive research. “I think there’s a lot more to do with neuroscience, especially with artificial intelligence. There are so many new technological developments happening right now,” he says.

Data transformed

by Jennifer Michalowski |

With the tools of modern neuroscience, data accumulates quickly. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of cells’ elaborately branched paths. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

“When I entered neuroscience about 20 years ago, data were extremely precious, and ideas, as the expression went, were cheap. That’s no longer true,” says McGovern Associate Investigator Ila Fiete. “We have an embarrassment of wealth in the data but lack sufficient conceptual and mathematical scaffolds to understand it.”

Fiete will lead the McGovern Institute’s new K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, whose scientists will create mathematical models and other computational tools to confront the current deluge of data and advance our understanding of the brain and mental health. The center, funded by a $24 million donation from philanthropist Lisa Yang, will take a uniquely collaborative approach to computational neuroscience, integrating data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

“Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by this center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

Data integration

Fiete says computation is particularly crucial to neuroscience because the brain is so staggeringly complex. Its billions of neurons, which are themselves complicated and diverse, interact with one other through trillions of connections.

“Conceptually, it’s clear that all these interactions are going to lead to pretty complex things. And these are not going to be things that we can explain in stories that we tell,” Fiete says. “We really will need mathematical models. They will allow us to ask about what changes when we perturb one or several components — greatly accelerating the rate of discovery relative to doing those experiments in real brains.”

By representing the interactions between the components of a neural circuit, a model gives researchers the power to explore those interactions, manipulate them, and predict the circuit’s behavior under different conditions.

“You can observe these neurons in the same way that you would observe real neurons. But you can do even more, because you have access to all the neurons and you have access to all the connections and everything in the network,” explains computational neuroscientist and McGovern Associate Investigator Guangyu Robert Yang (no relation to Lisa Yang), who joined MIT as a junior faculty member in July 2021.

Many neuroscience models represent specific functions or parts of the brain. But with advances in computation and machine learning, along with the widespread availability of experimental data with which to test and refine models, “there’s no reason that we should be limited to that,” he says.

Robert Yang’s team at the McGovern Institute is working to develop models that integrate multiple brain areas and functions. “The brain is not just about vision, just about cognition, just about motor control,” he says. “It’s about all of these things. And all these areas, they talk to one another.” Likewise, he notes, it’s impossible to separate the molecules in the brain from their effects on behavior – although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise.

The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain. To foster interdisciplinary collaboration, every postdoctoral fellow and engineer at the center will work with multiple faculty mentors. Working in three closely interacting scientific cores, fellows will develop computational technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify pat-terns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies will also help researchers model neural circuits, ultimately transforming data into knowledge and understanding.

“Lisa is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.”

Computational modeling

In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease.

These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies. “I really think that the future of treating disorders of the mind is going to run through computational modeling,” says McGovern Associate Investigator Josh McDermott.

In McDermott’s lab, researchers are modeling the brain’s auditory circuits. “If we had a perfect model of the auditory system, we would be able to understand why when somebody loses their hearing, auditory abilities degrade in the very particular ways in which they degrade,” he says. Then, he says, that model could be used to optimize hearing aids by predicting how the brain would interpret sound altered in various ways by the device.

Similar opportunities will arise as researchers model other brain systems, McDermott says, noting that computational models help researchers grapple with a dauntingly vast realm of possibilities. “There’s lots of different ways the brain can be set up, and lots of different potential treatments, but there is a limit to the number of neuroscience or behavioral experiments you can run,” he says. “Doing experiments on a computational system is cheap, so you can explore the dynamics of the system in a very thorough way.”

The ICoN Center will speed the development of the computational tools that neuroscientists need, both for basic understanding of the brain and clinical advances. But Fiete hopes for a culture shift within neuroscience, as well. “There are a lot of brilliant students and postdocs who have skills that are mathematics and computational and modeling based,” she says. “I think once they know that there are these possibilities to collaborate to solve problems related to psychiatric disorders and how we think, they will see that this is an exciting place to apply their skills, and we can bring them in.”

New integrative computational neuroscience center established at MIT’s McGovern Institute

by Jennifer Michalowski |

With the tools of modern neuroscience, researchers can peer into the brain with unprecedented accuracy. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Genetic tools allow us to focus on specific types of neurons based on their molecular signatures. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of elaborately branched dendrites. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

This deluge of data provides insights into brain function and dynamics at different levels – molecules, cells, circuits, and behavior — but the insights often remain compartmentalized in separate research silos. An innovative new center at MIT’s McGovern Institute aims to leverage them into powerful revelations of the brain’s inner workings.

The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will create advanced mathematical models and computational tools to synthesize the deluge of data across scales and advance our understanding of the brain and mental health.

The center, funded by a $24 million donation from philanthropist Lisa Yang and led by McGovern Institute Associate Investigator Ila Fiete, will take a collaborative approach to computational neuroscience, integrating cutting-edge modeling techniques and data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

“Our goal is that sophisticated, truly integrated computational models of the brain will make it possible to identify how ‘control knobs’ such as genes, proteins, chemicals, and environment drive thoughts and behavior, and to make inroads toward urgent unmet needs in understanding and treating brain disorders,” says Fiete, who is also a brain and cognitive sciences professor at MIT.

“Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by the ICoN center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

Connecting the data

It is impossible to separate the molecules in the brain from their effects on behavior – although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise. The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain.

“The center’s highly collaborative structure, which is essential for unifying multiple levels of understanding, will enable us to recruit talented young scientists eager to revolutionize the field of computational neuroscience,” says Robert Desimone, director of the McGovern Institute. “It is our hope that the ICoN Center’s unique research environment will truly demonstrate a new academic research structure that catalyzes bold, creative research.”

To foster interdisciplinary collaboration, every postdoctoral fellow and engineer at the center will work with multiple faculty mentors. In order to attract young scientists and engineers to the field of computational neuroscience, the center will also provide four graduate fellowships to MIT students each year in perpetuity. Interacting closely with three scientific cores, engineers and fellows will develop computational models and technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify patterns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies and models will be instrumental in synthesizing data into knowledge and understanding.

Center priorities

In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. Models of complex behavior will be created in collaboration with clinicians and researchers at Children’s Hospital of Philadelphia.

The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease. These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies.

“Lisa Yang is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.”

 

Ila Fiete studies how the brain performs complex computations

Anne Trafton |

While doing a postdoc about 15 years ago, Ila Fiete began searching for faculty jobs in computational neuroscience — a field that uses mathematical tools to investigate brain function. However, there were no advertised positions in theoretical or computational neuroscience at that time in the United States.

“It wasn’t really a field,” she recalls. “That has changed completely, and [now] there are 15 to 20 openings advertised per year.” She ended up finding a position in the Center for Learning and Memory at the University of Texas at Austin, which along with a small handful of universities including MIT, was open to neurobiologists with a computational background.

Computation is the cornerstone of Fiete’s research at MIT’s McGovern Institute for Brain Research, where she has been a faculty member since 2018. Using computational and mathematical techniques, she studies how the brain encodes information in ways that enable cognitive tasks such as learning, memory, and reasoning about our surroundings.

One major research area in Fiete’s lab is how the brain is able to continuously compute the body’s position in space and make constant adjustments to that estimate as we move about.

“When we walk through the world, we can close our eyes and still have a pretty good estimate of where we are,” she says. “This involves being able to update our estimate based on our sense of self-motion. There are also many computations in the brain that involve moving through abstract or mental rather than physical space, and integrating velocity signals of some variety or another. Some of the same ideas and even circuits for spatial navigation might be involved in navigating through these mental spaces.”

No better fit

Fiete spent her childhood between Mumbai, India, and the United States, where her mathematician father held a series of visiting or permanent appointments at the Institute for Advanced Study in Princeton, NJ, the University of California at Berkeley, and the University of Michigan at Ann Arbor.

In India, Fiete’s father did research at the Tata Institute of Fundamental Research, and she grew up spending time with many other children of academics. She was always interested in biology, but also enjoyed math, following in her father’s footsteps.

“My father was not a hands-on parent, wanting to teach me a lot of mathematics, or even asking me about how my math schoolwork was going, but the influence was definitely there. There’s a certain aesthetic to thinking mathematically, which I absorbed very indirectly,” she says. “My parents did not push me into academics, but I couldn’t help but be influenced by the environment.”

She spent her last two years of high school in Ann Arbor and then went to the University of Michigan, where she majored in math and physics. While there, she worked on undergraduate research projects, including two summer stints at Indiana University and the University of Virginia, which gave her firsthand experience in physics research. Those projects covered a range of topics, including proton radiation therapy, the magnetic properties of single crystal materials, and low-temperature physics.

“Those three experiences are what really made me sure that I wanted to go into academics,” Fiete says. “It definitely seemed like the path that I knew the best, and I think it also best suited my temperament. Even now, with more exposure to other fields, I cannot think of a better fit.”

Although she was still interested in biology, she took only one course in the subject in college, holding back because she did not know how to marry quantitative approaches with biological sciences. She began her graduate studies at Harvard University planning to study low-temperature physics, but while there, she decided to start explore quantitative classes in biology. One of those was a systems biology course taught by then-MIT professor Sebastian Seung, which transformed her career trajectory.

“It was truly inspiring,” she recalls. “Thinking mathematically about interacting systems in biology was really exciting. It was really my first introduction to systems biology, and it had me hooked immediately.”

She ended up doing most of her PhD research in Seung’s lab at MIT, where she studied how the brain uses incoming signals of the velocity of head movement to control eye position. For example, if we want to keep our gaze fixed on a particular location while our head is moving, the brain must continuously calculate and adjust the amount of tension needed in the muscles surrounding the eyes, to compensate for the movement of the head.

“Bizarre” cells

After earning her PhD, Fiete and her husband, a theoretical physicist, went to the Kavli Institute for Theoretical Physics at the University of California at Santa Barbara, where they each held fellowships for independent research. While there, Fiete began working on a research topic that she still studies today — grid cells. These cells, located in the entorhinal cortex of the brain, enable us to navigate our surroundings by helping the brain to create a neural representation of space.

Midway through her position there, she learned of a new discovery, that when a rat moves across an open room, a grid cell in its brain 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 the entire room. These cells have also been found in the brains of various other mammals, including humans.

“It’s amazing. It’s this very crystalline response,” Fiete says. “When I read about that, I fell out of my chair. At that point I knew this was something bizarre that would generate so many questions about development, function, and brain circuitry that could be studied computationally.”

One question Fiete and others have investigated is why the brain needs grid cells at all, since it also has so-called place cells that each fire in one specific location in the environment. A possible explanation that Fiete has explored is that grid cells of different scales, working together, can represent a vast number of possible positions in space and also multiple dimensions of space.

“If you have a few cells that can parsimoniously generate a very large coding space, then you can afford to not use most of that coding space,” she says. “You can afford to waste most of it, which means you can separate things out very well, in which case it becomes not so susceptible to noise.”

Since returning to MIT, she has also pursued a research theme related to what she explored in her PhD thesis — how the brain maintains neural representations of where the head is located in space. In a paper published last year, she uncovered that the brain generates a one-dimensional ring of neural activity that acts as a compass, allowing the brain to calculate the current direction of the head relative to the external world.

Her lab also studies cognitive flexibility — the brain’s ability to perform so many different types of cognitive tasks.

“How it is that we can repurpose the same circuits and flexibly use them to solve many different problems, and what are the neural codes that are amenable to that kind of reuse?” she says. “We’re also investigating the principles that allow the brain to hook multiple circuits together to solve new problems without a lot of reconfiguration.”

Family members unite to fight COVID-19

by Sugandha Sharma |

Even before MIT sent out its first official announcement about the COVID-19 crisis, I had already asked permission from my supervisor and taken my computer home so that I could start working from home.

My first and foremost concern was my family and friends. I was born and brought up in India, and then immigrated to Canada, so I have a big and wonderful family spread across both those countries. These countries had a lower number of COVID-19 cases at the time, but I could see what would be coming their way. I was anxious, very anxious. In India, my dad being an anesthetist could be exposed while working in the hospital. In Canada, my uncle who is a physician could be exposed, and on top of that he lives in the same house as my grandparents who are even more vulnerable due to their age. I knew I had to do something.

We started having regular video calls as a family. My mom even led daily online yoga sessions, and the discussions that followed those sessions ensured that we didn’t feel lonely and gave us a sense of purpose. Together, we looked at the statistics in the data from China and Italy, and learned that we needed to flatten the curve due to the lack of medical resources required to meet the need of the hour. We could foresee that more infections would lead to more patients, thus raising the demand for medical resources beyond the amount we had available.

We had several discussions around developing products for helping medical professionals and the general public during this pandemic.

We learned that since no government has enough resources to cope at the time of pandemics, we have to be innovative in trying to make the best use of the limited resources available to us.

Through our discussions and experiences of some of us in the field, we came to the conclusion that the only way to effectively fight COVID-19 is prevention at source. Hence, we started working on a mobile app that uses AI and advanced data analytics to trace contact, determine the risk of infection, and thereby suggest precautions. Luckily we have engineers and computer scientists in our family (my own background is in electrical engineering), so it was easy for us to divide the work.  In our prototype, when people sign-up, they are asked to fill out a short self-assessment form that can be used to identify any symptoms of COVID-19. This data is then used to predict vulnerable areas and to give recommendations to people who might have taken a certain route as shown below.

Sharma’s mobile app showing heatmap of the vulnerable areas in a locality in Toronto, ON (left) and personalized recommendations based on the most recent route taken by an individual (right).

We ended up submitting our proposal and prototype to the COVID-19 challenge launched by Vale (a global mining company) and the winners will be announced in May.

Personally, to be completely honest, I had my times when I broke down due to everything that was going on in the world around me. It’s not easy to see people dying, and losing jobs. My way of staying strong was to make sure that I was doing my best to contribute.

I have set up a beautiful home office for myself and I am focusing on my PhD research, being grateful that I can still continue to do it from home. I have also restarted the joint MIT-Harvard computational neuroscience journal club meetings online, so that members can get access to this wonderful community once again! It was amazing to see from a poll we conducted that 92% of the members of the club wanted the meetings to be re-started online.

These times are unprecedented for my generation, my mom’s generation and even for my grandmother’s generation. I have never seen the world come together in a way I have seen during this pandemic. The kind of response we have seen from our societies and governments across the globe shows that we can make intelligent decisions for the collective good of humanity. For once, we’re all on the same side!

Sugandha (Su) Sharma is a graduate student in the labs of Ila Fiete and Josh Tenenbaum. When she’s not developing a mobile app to fight COVID-19, Su explores the computational and theoretical principles underlying higher level cognition and intelligence in the human brain.

#WeAreMcGovern

Finding the brain’s compass

by Sabbi Lall |

The world is constantly bombarding our senses with information, but the ways in which our brain extracts meaning from this information remains elusive. How do neurons transform raw visual input into a mental representation of an object – like a chair or a dog?

In work published today in Nature Neuroscience, MIT neuroscientists have identified a brain circuit in mice that distills “high-dimensional” complex information about the environment into a simple abstract object in the brain.

“There are no degree markings in the external world, our current head direction has to be extracted, computed, and estimated by the brain,” explains Ila Fiete, an associate member of the McGovern Institute and senior author of the paper. “The approaches we used allowed us to demonstrate the emergence of a low-dimensional concept, essentially an abstract compass in the brain.”

This abstract compass, according to the researchers, is a one-dimensional ring that represents the current direction of the head relative to the external world.

Schooling fish

Trying to show that a data cloud has a simple shape, like a ring, is a bit like watching a school of fish. By tracking one or two sardines, you might not see a pattern. But if you could map all of the sardines, and transform the noisy dataset into points representing the positions of the whole school of sardines over time, and where each fish is relative to its neighbors, a pattern would emerge. This model would reveal a ring shape, a simple shape formed by the activity of hundreds of individual fish.

Fiete, who is also an associate professor in MIT’s Department of Brain and Cognitive Sciences, used a similar approach, called topological modeling, to transform the activity of large populations of noisy neurons into a data cloud the shape of a ring.

Simple and persistent ring

Previous work in fly brains revealed a physical ellipsoid ring of neurons representing changes in the direction of the fly’s head, and researchers suspected that such a system might also exist in mammals.

In this new mouse study, Fiete and her colleagues measured hours of neural activity from scores of neurons in the anterodorsal thalamic nucleus (ADN) – a region believed to play a role in spatial navigation – as the animals moved freely around their environment. They mapped how the neurons in the ADN circuit fired as the animal’s head changed direction.

Together these data points formed a cloud in the shape of a simple and persistent ring.

“In the absence of this ring,” Fiete explains, “we would be lost in the world.”

“This tells us a lot about how neural networks are organized in the brain,” explains Edvard Moser, Director of the Kavli Institute of Systems Neuroscience in Norway, who was not involved in the study. “Past data have indirectly pointed towards such a ring-like organization but only now has it been possible, with the right cell numbers and methods, to demonstrate it convincingly,” says Moser.

Their method for characterizing the shape of the data cloud allowed Fiete and colleagues to determine which variable the circuit was devoted to representing, and to decode this variable over time, using only the neural responses.

“The animal’s doing really complicated stuff,” explains Fiete, “but this circuit is devoted to integrating the animal’s speed along a one-dimensional compass that encodes head direction,” explains Fiete. “Without a manifold approach, which captures the whole state space, you wouldn’t know that this circuit of thousands of neurons is encoding only this one aspect of the complex behavior, and not encoding any other variables at the same time.”

Even during sleep, when the circuit is not being bombarded with external information, this circuit robustly traces out the same one-dimensional ring, as if dreaming of past head direction trajectories.

Further analysis revealed that the ring acts an attractor. If neurons stray off trajectory, they are drawn back to it, quickly correcting the system. This attractor property of the ring means that the representation of head direction in abstract space is reliably stable over time, a key requirement if we are to understand and maintain a stable sense of where our head is relative to the world around us.

“In the absence of this ring,” Fiete explains, “we would be lost in the world.”

Shaping the future

Fiete’s work provides a first glimpse into how complex sensory information is distilled into a simple concept in the mind, and how that representation autonomously corrects errors, making it exquisitely stable.

But the implications of this study go beyond coding of head direction.

“Similar organization is probably present for other cognitive functions so the paper is likely to inspire numerous new studies,” says Moser.

Fiete sees these analyses and related studies carried out by colleagues at the Norwegian University of Science and Technology, Princeton University, the Weitzman Institute, and elsewhere as fundamental to the future of neural decoding studies.

With this approach, she explains, it is possible to extract abstract representations of the mind from the brain, potentially even thoughts and dreams.

“We’ve found that the brain deconstructs and represents complex things in the world with simple shapes,” explains Fiete. “Manifold-level analysis can help us to find those shapes, and they almost certainly exist beyond head direction circuits.”

Ila Fiete joins the McGovern Institute

by Sabbi Lall |

Ila Fiete, an associate professor in the Department of Brain and Cognitive Sciences at MIT recently joined the McGovern Institute as an associate investigator. Fiete is working to understand the circuits that underlie short-term memory, integration, and inference in the brain.

Think about the simple act of visiting a new town and getting to know its layout as you explore it. What places are reachable from others? Where are landmarks relative to each other? Where are you relative to these landmarks? How do you get from here to where you want to go next?

The process that occurs as your brain tries to transform the few routes you follow into a coherent map of the world is just one of myriad examples of hard computations that the brain is constantly performing. Fiete’s goal is to understand how the brain is able to carry out such computations, and she is developing and using multiple tools to this end. These approaches include pure theoretical approaches to examine neural codes, building numerical dynamical models of circuit operation, and techniques to extract information about the underlying circuit dynamics from neural data.

Spatial navigation is a particularly interesting nut to crack from a neural perspective: The mapping devices on your phone have access to global satellite data, previously constructed detailed maps of the town, various additional sensors, and excellent non-leaky memory. By contrast, the brain must build maps, plan routes, and determine goals all using noisy, local sensors, no externally provided maps, and with noisy, forgetful or leaky neurons. Fiete is particularly interested in elucidating how the brain deals with noisy and ambiguous cues from the world to arrive at robust estimates about the world that resolve ambiguity. She is also interested in how the networks that are important for memory and integration develop through plasticity, learning, and development in the brain.

Fiete earned a BS in mathematics and physics at the University of Michigan then obtained her PhD in 2004 at Harvard University in the Department of Physics. She held a postdoctoral appointment at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara from 2004 to 2006, while she was also a visiting member of the Center for Theoretical Biophysics at the University of California at San Diego. Fiete subsequently spent two years at Caltech as a Broad Fellow in brain circuitry, and in 2008 joined the faculty of the University of Texas at Austin. She is currently an HHMI faculty scholar.

Ila Fiete

Neural Coding and Dynamics

Ila Fiete builds theoretical models and tools that are elucidating computations performed by the brain as it interacts with the world. Her focus includes describing how plasticity and development shape networks to perform computation and how the brain represents and manipulates information. She works closely with collaborators to design experiments that allow analysis of how the brain solves complex tasks, such as spatial navigation. By combining theoretical insights with predictions and designs for experiment, Fiete aims to better understand how the brain constructs and uses memory for spatial and non-spatial reasoning, the mechanisms for error control in neural codes, and rules for synaptic plasticity that enable neural circuit organization. Through these avenues, she hopes to better understand the circuits underlying phenomena including short-term memory, integration, and inference, navigation, and reasoning in the brain.

School of Science welcomes 10 professors

by School of Science |

The MIT School of Science recently welcomed 10 new professors, including Ila Fiete in the departments of Brain and Cognitive Sciences, Chemistry, Biology, Physics, Mathematics, and Earth, Atmospheric and Planetary Sciences.

Ila Fiete uses computational and theoretical tools to better understand the dynamical mechanisms and coding strategies that underlie computation in the brain, with a focus on elucidating how plasticity and development shape networks to perform computation and why information is encoded the way that it is. Her recent focus is on error control in neural codes, rules for synaptic plasticity that enable neural circuit organization, and questions at the nexus of information and dynamics in neural systems, such as understand how coding and statistics fundamentally constrain dynamics and vice-versa.

Tristan Collins conducts research at the intersection of geometric analysis, partial differential equations, and algebraic geometry. In joint work with Valentino Tosatti, Collins described the singularity formation of the Ricci flow on Kahler manifolds in terms of algebraic data. In recent work with Gabor Szekelyhidi, he gave a necessary and sufficient algebraic condition for existence of Ricci-flat metrics, which play an important role in string theory and mathematical physics. This result lead to the discovery of infinitely many new Einstein metrics on the 5-dimensional sphere. With Shing-Tung Yau and Adam Jacob, Collins is currently studying the relationship between categorical stability conditions and existence of solutions to differential equations arising from mirror symmetry.

Collins earned his BS in mathematics at the University of British Columbia in 2009, after which he completed his PhD in mathematics at Columbia University in 2014 under the direction of Duong H. Phong. Following a four-year appointment as a Benjamin Peirce Assistant Professor at Harvard University, Collins joins MIT as an assistant professor in the Department of Mathematics.

Julien de Wit develops and applies new techniques to study exoplanets, their atmospheres, and their interactions with their stars. While a graduate student in the Sara Seager group at MIT, he developed innovative analysis techniques to map exoplanet atmospheres, studied the radiative and tidal planet-star interactions in eccentric planetary systems, and constrained the atmospheric properties and mass of exoplanets solely from transmission spectroscopy. He plays a critical role in the TRAPPIST/SPECULOOS project, headed by Université of Liège, leading the atmospheric characterization of the newly discovered TRAPPIST-1 planets, for which he has already obtained significant results with the Hubble Space Telescope. De Wit’s efforts are now also focused on expanding the SPECULOOS network of telescopes in the northern hemisphere to continue the search for new potentially habitable TRAPPIST-1-like systems.

De Wit earned a BEng in physics and mechanics from the Université de Liège in Belgium in 2008, an MS in aeronautic engineering and an MRes in astrophysics, planetology, and space sciences from the Institut Supérieur de l’Aéronautique et de l’Espace at the Université de Toulouse, France in 2010; he returned to the Université de Liège for an MS in aerospace engineering, completed in 2011. After finishing his PhD in planetary sciences in 2014 and a postdoc at MIT, both under the direction of Sara Seager, he joins the MIT faculty in the Department of Earth, Atmospheric and Planetary Sciences as an assistant professor.

After earning a BS in mathematics and physics at the University of Michigan, Fiete obtained her PhD in 2004 at Harvard University in the Department of Physics. While holding an appointment at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara from 2004 to 2006, she was also a visiting member of the Center for Theoretical Biophysics at the University of California at San Diego. Fiete subsequently spent two years at Caltech as a Broad Fellow in brain circuitry, and in 2008 joined the faculty of the University of Texas at Austin. She joins the MIT faculty in the Department of Brain and Cognitive Sciences as an associate professor with tenure.

Ankur Jain explores the biology of RNA aggregation. Several genetic neuromuscular disorders, such as myotonic dystrophy and amyotrophic lateral sclerosis, are caused by expansions of nucleotide repeats in their cognate disease genes. Such repeats cause the transcribed RNA to form pathogenic clumps or aggregates. Jain uses a variety of biophysical approaches to understand how the RNA aggregates form, and how they can be disrupted to restore normal cell function. Jain will also study the role of RNA-DNA interactions in chromatin organization, investigating whether the RNA transcribed from telomeres (the protective repetitive sequences that cap the ends of chromosomes) undergoes the phase separation that characterizes repeat expansion diseases.

Jain completed a bachelor’s of technology degree in biotechnology and biochemical engineering at the Indian Institute of Technology Kharagpur, India in 2007, followed by a PhD in biophysics and computational biology at the University of Illinois at Urbana-Champaign under the direction of Taekjip Ha in 2013. After a postdoc at the University of California at San Francisco, he joins the MIT faculty in the Department of Biology as an assistant professor with an appointment as a member of the Whitehead Institute for Biomedical Research.

Kiyoshi Masui works to understand fundamental physics and the evolution of the universe through observations of the large-scale structure — the distribution of matter on scales much larger than galaxies. He works principally with radio-wavelength surveys to develop new observational methods such as hydrogen intensity mapping and fast radio bursts. Masui has shown that such observations will ultimately permit precise measurements of properties of the early and late universe and enable sensitive searches for primordial gravitational waves. To this end, he is working with a new generation of rapid-survey digital radio telescopes that have no moving parts and rely on signal processing software running on large computer clusters to focus and steer, including work on the Canadian Hydrogen Intensity Mapping Experiment (CHIME).

Masui obtained a BSCE in engineering physics at Queen’s University, Canada in 2008 and a PhD in physics at the University of Toronto in 2013 under the direction of Ue-Li Pen. After postdoctoral appointments at the University of British Columbia as the Canadian Institute for Advanced Research Global Scholar and the Canadian Institute for Theoretical Astrophysics National Fellow, Masui joins the MIT faculty in the Department of Physics as an assistant professor.

Phiala Shanahan studies theoretical nuclear and particle physics, in particular the structure and interactions of hadrons and nuclei from the fundamental (quark and gluon) degrees of freedom encoded in the Standard Model of particle physics. Shanahan’s recent work has focused on the role of gluons, the force carriers of the strong interactions described by quantum chromodynamics (QCD), in hadron and nuclear structure by using analytic tools and high-performance supercomputing. She recently achieved the first calculation of the gluon structure of light nuclei, making predictions that will be testable in new experiments proposed at Jefferson National Accelerator Facility and at the planned Electron-Ion Collider. She has also undertaken extensive studies of the role of strange quarks in the proton and light nuclei that sharpen theory predictions for dark matter cross-sections in direct detection experiments. To overcome computational limitations in QCD calculations for hadrons and in particular for nuclei, Shanahan is pursuing a program to integrate modern machine learning techniques in computational nuclear physics studies.

Shanahan obtained her BS in 2012 and her PhD in 2015, both in physics, from the University of Adelaide. She completed postdoctoral work at MIT in 2017, then held a joint position as an assistant professor at the College of William and Mary and senior staff scientist at the Thomas Jefferson National Accelerator Facility until 2018. She returns to MIT in the Department of Physics as an assistant professor.

Nike Sun works in probability theory at the interface of statistical physics and computation. Her research focuses in particular on phase transitions in average-case (randomized) formulations of classical computational problems. Her joint work with Jian Ding and Allan Sly establishes the satisfiability threshold of random k-SAT for large k, and relatedly the independence ratio of random regular graphs of large degree. Both are long-standing open problems where heuristic methods of statistical physics yield detailed conjectures, but few rigorous techniques exist. More recently she has been investigating phase transitions of dense graph models.

Sun completed BA mathematics and MA statistics degrees at Harvard in 2009, and an MASt in mathematics at Cambridge in 2010. She received her PhD in statistics from Stanford University in 2014 under the supervision of Amir Dembo. She held a Schramm fellowship at Microsoft New England and MIT Mathematics in 2014-2015 and a Simons postdoctoral fellowship at the University of California at Berkeley in 2016, and joined the Berkeley Department of Statistics as an assistant professor in 2016. She returns to the MIT Department of Mathematics as an associate professor with tenure.

Alison Wendlandt focuses on the development of selective, catalytic reactions using the tools of organic and organometallic synthesis and physical organic chemistry. Mechanistic study plays a central role in the development of these new transformations. Her projects involve the design of new catalysts and catalytic transformations, identification of important applications for selective catalytic processes, and elucidation of new mechanistic principles to expand powerful existing catalytic reaction manifolds.

Wendlandt received a BS in chemistry and biological chemistry from the University of Chicago in 2007, an MS in chemistry from Yale University in 2009, and a PhD in chemistry from the University of Wisconsin at Madison in 2015 under the direction of Shannon S. Stahl. Following an NIH Ruth L. Krichstein Postdoctoral Fellowship at Harvard University, Wendlandt joins the MIT faculty in the Department of Chemistry as an assistant professor.

Chenyang Xu specializes in higher-dimensional algebraic geometry, an area that involves classifying algebraic varieties, primarily through the minimal model program (MMP). MMP was introduced by Fields Medalist S. Mori in the early 1980s to make advances in higher dimensional birational geometry. The MMP was further developed by Hacon and McKernan in the mid-2000s, so that the MMP could be applied to other questions. Collaborating with Hacon, Xu expanded the MMP to varieties of certain conditions, such as those of characteristic p, and, with Hacon and McKernan, proved a fundamental conjecture on the MMP, generating a great deal of follow-up activity. In collaboration with Chi Li, Xu proved a conjecture of Gang Tian concerning higher-dimensional Fano varieties, a significant achievement. In a series of papers with different collaborators, he successfully applied MMP to singularities.

Xu received his BS in 2002 and MS in 2004 in mathematics from Peking University, and completed his PhD at Princeton University under János Kollár in 2008. He came to MIT as a CLE Moore Instructor in 2008-2011, and was subsequently appointed assistant professor at the University of Utah. He returned to Peking University as a research fellow at the Beijing International Center of Mathematical Research in 2012, and was promoted to professor in 2013. Xu joins the MIT faculty as a full professor in the Department of Mathematics.

Zhiwei Yun’s research is at the crossroads between algebraic geometry, number theory, and representation theory. He studies geometric structures aiming at solving problems in representation theory and number theory, especially those in the Langlands program. While he was a CLE Moore Instructor at MIT, he started to develop the theory of rigid automorphic forms, and used it to answer an open question of J-P Serre on motives, which also led to a major result on the inverse Galois problem in number theory. More recently, in his joint work with Wei Zhang, they give geometric interpretation of higher derivatives of automorphic L- functions in terms of intersection numbers, which sheds new light on the geometric analogue of the Birch and Swinnerton-Dyer conjecture.

Yun earned his BS at Peking University in 2004, after which he completed his PhD at Princeton University in 2009 under the direction of Robert MacPherson. After appointments at the Institute for Advanced Study and as a CLE Moore Instructor at MIT, he held faculty appointments at Stanford and Yale. He returned to the MIT Department of Mathematics as a full professor in the spring of 2018.