Approaching human cognition from many angles

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.

Dendrites may help neurons perform complicated calculations

Within the human brain, neurons perform complex calculations on information they receive. Researchers at MIT have now demonstrated how dendrites — branch-like extensions that protrude from neurons — help to perform those computations.

The researchers found that within a single neuron, different types of dendrites receive input from distinct parts of the brain, and process it in different ways. These differences may help neurons to integrate a variety of inputs and generate an appropriate response, the researchers say.

In the neurons that the researchers examined in this study, it appears that this dendritic processing helps cells to take in visual information and combine it with motor feedback, in a circuit that is involved in navigation and planning movement.

“Our hypothesis is that these neurons have the ability to pick out specific features and landmarks in the visual environment, and combine them with information about running speed, where I’m going, and when I’m going to start, to move toward a goal position,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Mathieu Lafourcade, a former MIT postdoc, is the lead author of the paper, which appears today in Neuron.

Complex calculations

Any given neuron can have dozens of dendrites, which receive synaptic input from other neurons. Neuroscientists have hypothesized that these dendrites can act as compartments that perform their own computations on incoming information before sending the results to the body of the neuron, which integrates all these signals to generate an output.

Previous research has shown that dendrites can amplify incoming signals using specialized proteins called NMDA receptors. These are voltage-sensitive neurotransmitter receptors that are dependent on the activity of other receptors called AMPA receptors. When a dendrite receives many incoming signals through AMPA receptors at the same time, the threshold to activate nearby NMDA receptors is reached, creating an extra burst of current.

This phenomenon, known as supralinearity, is believed to help neurons distinguish between inputs that arrive close together or farther apart in time or space, Harnett says.

In the new study, the MIT researchers wanted to determine whether different types of inputs are targeted specifically to different types of dendrites, and if so, how that would affect the computations performed by those neurons. They focused on a population of neurons called pyramidal cells, the principal output neurons of the cortex, which have several different types of dendrites. Basal dendrites extend below the body of the neuron, apical oblique dendrites extend from a trunk that travels up from the body, and tuft dendrites are located at the top of the trunk.

Harnett and his colleagues chose a part of the brain called the retrosplenial cortex (RSC) for their studies because it is a good model for association cortex — the type of brain cortex used for complex functions such as planning, communication, and social cognition. The RSC integrates information from many parts of the brain to guide navigation, and pyramidal neurons play a key role in that function.

In a study of mice, the researchers first showed that three different types of input come into pyramidal neurons of the RSC: from the visual cortex into basal dendrites, from the motor cortex into apical oblique dendrites, and from the lateral nuclei of the thalamus, a visual processing area, into tuft dendrites.

“Until now, there hasn’t been much mapping of what inputs are going to those dendrites,” Harnett says. “We found that there are some sophisticated wiring rules here, with different inputs going to different dendrites.”

A range of responses

The researchers then measured electrical activity in each of those compartments. They expected that NMDA receptors would show supralinear activity, because this behavior has been demonstrated before in dendrites of pyramidal neurons in both the primary sensory cortex and the hippocampus.

In the basal dendrites, the researchers saw just what they expected: Input coming from the visual cortex provoked supralinear electrical spikes, generated by NMDA receptors. However, just 50 microns away, in the apical oblique dendrites of the same cells, the researchers found no signs of supralinear activity. Instead, input to those dendrites drives a steady linear response. Those dendrites also have a much lower density of NMDA receptors.

“That was shocking, because no one’s ever reported that before,” Harnett says. “What that means is the apical obliques don’t care about the pattern of input. Inputs can be separated in time, or together in time, and it doesn’t matter. It’s just a linear integrator that’s telling the cell how much input it’s getting, without doing any computation on it.”

Those linear inputs likely represent information such as running speed or destination, Harnett says, while the visual information coming into the basal dendrites represents landmarks or other features of the environment. The supralinearity of the basal dendrites allows them to perform more sophisticated types of computation on that visual input, which the researchers hypothesize allows the RSC to flexibly adapt to changes in the visual environment.

In the tuft dendrites, which receive input from the thalamus, it appears that NMDA spikes can be generated, but not very easily. Like the apical oblique dendrites, the tuft dendrites have a low density of NMDA receptors. Harnett’s lab is now studying what happens in all of these different types of dendrites as mice perform navigation tasks.

The research was funded by a Boehringer Ingelheim Fonds PhD Fellowship, the National Institutes of Health, the James W. and Patricia T. Poitras Fund, the Klingenstein-Simons Fellowship Program, a Vallee Scholar Award, and a McKnight Scholar Award.

School of Science announces 2022 Infinite Expansion Awards

The MIT School of Science has announced eight postdocs and research scientists as recipients of the 2022 Infinite Expansion Award.

The award, formerly known as the Infinite Kilometer Award, was created in 2012 to highlight extraordinary members of the MIT science community. The awardees are nominated not only for their research, but for going above and beyond in mentoring junior colleagues, participating in educational programs, and contributing to their departments, labs, and research centers, the school, and the Institute.

The 2022 School of Science Infinite Expansion winners are:

  • Héctor de Jesús-Cortés, a postdoc in the Picower Institute for Learning and Memory, nominated by professor and Department of Brain and Cognitive Sciences (BCS) head Michale Fee, professor and McGovern Institute for Brain Research Director Robert Desimone, professor and Picower Institute Director Li-Huei Tsai, professor and associate BCS head Laura Schulz, associate professor and associate BCS head Joshua McDermott, and professor and BCS Postdoc Officer Mark Bear for his “awe-inspiring commitment of time and energy to research, outreach, education, mentorship, and community;”
  • Harold Erbin, a postdoc in the Laboratory for Nuclear Science’s Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), nominated by professor and IAIFI Director Jesse Thaler, associate professor and IAIFI Deputy Director Mike Williams, and associate professor and IAIFI Early Career and Equity Committee Chair Tracy Slatyer for “provid[ing] exemplary service on the IAIFI Early Career and Equity Committee” and being “actively involved in many other IAIFI community building efforts;”
  • Megan Hill, a postdoc in the Department of Chemistry, nominated by Professor Jeremiah Johnson for being an “outstanding scientist” who has “also made exceptional contributions to our community through her mentorship activities and participation in Women in Chemistry;”
  • Kevin Kuns, a postdoc in the Kavli Institute for Astrophysics and Space Research, nominated by Associate Professor Matthew Evans for “consistently go[ing] beyond expectations;”
  • Xingcheng Lin, a postdoc in the Department of Chemistry, nominated by Associate Professor Bin Zhang for being “very talented, extremely hardworking, and genuinely enthusiastic about science;”
  • Alexandra Pike, a postdoc in the Department of Biology, nominated by Professor Stephen Bell for “not only excel[ing] in the laboratory” but also being “an exemplary citizen in the biology department, contributing to teaching, community, and to improving diversity, equity, and inclusion in the department;”
  • Nora Shipp, a postdoc with the Kavli Institute for Astrophysics and Space Research, nominated by Assistant Professor Lina Necib for being “independent, efficient, with great leadership qualities” with “impeccable” research; and
  • Jakob Voigts, a research scientist in the McGovern Institute for Brain Research, nominated by Associate Professor Mark Harnett and his laboratory for “contribut[ing] to the growth and development of the lab and its members in numerous and irreplaceable ways.”

Winners are honored with a monetary award and will be celebrated with family, friends, and nominators at a later date, along with recipients of the Infinite Mile Award.

Study finds a striking difference between neurons of humans and other mammals

McGovern Institute Investigator Mark Harnett. Photo: Justin Knight

Neurons communicate with each other via electrical impulses, which are produced by ion channels that control the flow of ions such as potassium and sodium. In a surprising new finding, MIT neuroscientists have shown that human neurons have a much smaller number of these channels than expected, compared to the neurons of other mammals.

The researchers hypothesize that this reduction in channel density may have helped the human brain evolve to operate more efficiently, allowing it to divert resources to other energy-intensive processes that are required to perform complex cognitive tasks.

“If the brain can save energy by reducing the density of ion channels, it can spend that energy on other neuronal or circuit processes,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Harnett and his colleagues analyzed neurons from 10 different mammals, the most extensive electrophysiological study of its kind, and identified a “building plan” that holds true for every species they looked at — except for humans. They found that as the size of neurons increases, the density of channels found in the neurons also increases.

However, human neurons proved to be a striking exception to this rule.

“Previous comparative studies established that the human brain is built like other mammalian brains, so we were surprised to find strong evidence that human neurons are special,” says former MIT graduate student Lou Beaulieu-Laroche.

Beaulieu-Laroche is the lead author of the study, which appears today in Nature.

A building plan

Neurons in the mammalian brain can receive electrical signals from thousands of other cells, and that input determines whether or not they will fire an electrical impulse called an action potential. In 2018, Harnett and Beaulieu-Laroche discovered that human and rat neurons differ in some of their electrical properties, primarily in parts of the neuron called dendrites — tree-like antennas that receive and process input from other cells.

One of the findings from that study was that human neurons had a lower density of ion channels than neurons in the rat brain. The researchers were surprised by this observation, as ion channel density was generally assumed to be constant across species. In their new study, Harnett and Beaulieu-Laroche decided to compare neurons from several different mammalian species to see if they could find any patterns that governed the expression of ion channels. They studied two types of voltage-gated potassium channels and the HCN channel, which conducts both potassium and sodium, in layer 5 pyramidal neurons, a type of excitatory neurons found in the brain’s cortex.


Former McGovern Institute graduate student Lou Beaulieu-Laroche is the lead author of the 2021 Nature paper.

They were able to obtain brain tissue from 10 mammalian species: Etruscan shrews (one of the smallest known mammals), gerbils, mice, rats, Guinea pigs, ferrets, rabbits, marmosets, and macaques, as well as human tissue removed from patients with epilepsy during brain surgery. This variety allowed the researchers to cover a range of cortical thicknesses and neuron sizes across the mammalian kingdom.

The researchers found that in nearly every mammalian species they looked at, the density of ion channels increased as the size of the neurons went up. The one exception to this pattern was in human neurons, which had a much lower density of ion channels than expected.

The increase in channel density across species was surprising, Harnett says, because the more channels there are, the more energy is required to pump ions in and out of the cell. However, it started to make sense once the researchers began thinking about the number of channels in the overall volume of the cortex, he says.

In the tiny brain of the Etruscan shrew, which is packed with very small neurons, there are more neurons in a given volume of tissue than in the same volume of tissue from the rabbit brain, which has much larger neurons. But because the rabbit neurons have a higher density of ion channels, the density of channels in a given volume of tissue is the same in both species, or any of the nonhuman species the researchers analyzed.

“This building plan is consistent across nine different mammalian species,” Harnett says. “What it looks like the cortex is trying to do is keep the numbers of ion channels per unit volume the same across all the species. This means that for a given volume of cortex, the energetic cost is the same, at least for ion channels.”

Energy efficiency

The human brain represents a striking deviation from this building plan, however. Instead of increased density of ion channels, the researchers found a dramatic decrease in the expected density of ion channels for a given volume of brain tissue.

The researchers believe this lower density may have evolved as a way to expend less energy on pumping ions, which allows the brain to use that energy for something else, like creating more complicated synaptic connections between neurons or firing action potentials at a higher rate.

“We think that humans have evolved out of this building plan that was previously restricting the size of cortex, and they figured out a way to become more energetically efficient, so you spend less ATP per volume compared to other species,” Harnett says.

He now hopes to study where that extra energy might be going, and whether there are specific gene mutations that help neurons of the human cortex achieve this high efficiency. The researchers are also interested in exploring whether primate species that are more closely related to humans show similar decreases in ion channel density.

The research was funded by the Natural Sciences and Engineering Research Council of Canada, a Friends of the McGovern Institute Fellowship, the National Institute of General Medical Sciences, the Paul and Daisy Soros Fellows Program, the Dana Foundation David Mahoney Neuroimaging Grant Program, the National Institutes of Health, the Harvard-MIT Joint Research Grants Program in Basic Neuroscience, and Susan Haar.

Other authors of the paper include Norma Brown, an MIT technical associate; Marissa Hansen, a former post-baccalaureate scholar; Enrique Toloza, a graduate student at MIT and Harvard Medical School; Jitendra Sharma, an MIT research scientist; Ziv Williams, an associate professor of neurosurgery at Harvard Medical School; Matthew Frosch, an associate professor of pathology and health sciences and technology at Harvard Medical School; Garth Rees Cosgrove, director of epilepsy and functional neurosurgery at Brigham and Women’s Hospital; and Sydney Cash, an assistant professor of neurology at Harvard Medical School and Massachusetts General Hospital.

Nine MIT students awarded 2021 Paul and Daisy Soros Fellowships for New Americans

An MIT senior and eight MIT graduate students are among the 30 recipients of this year’s P.D. Soros Fellowships for New Americans. In addition to senior Fiona Chen, MIT’s newest Soros winners include graduate students Aziza Almanakly, Alaleh Azhir, Brian Y. Chang PhD ’18, James Diao, Charlie ChangWon Lee, Archana Podury, Ashwin Sah ’20, and Enrique Toloza. Six of the recipients are enrolled at the Harvard-MIT Program in Health Sciences and Technology.

P.D. Soros Fellows receive up to $90,000 to fund their graduate studies and join a lifelong community of new Americans from different backgrounds and fields. The 2021 class was selected from a pool of 2,445 applicants, marking the most competitive year in the fellowship’s history.

The Paul & Daisy Soros Fellowships for New Americans program honors the contributions of immigrants and children of immigrants to the United States. As Fiona Chen says, “Being a new American has required consistent confrontation with the struggles that immigrants and racial minorities face in the U.S. today. It has meant frequent difficulties with finding security and comfort in new contexts. But it has also meant continual growth in learning to love the parts of myself — the way I look; the things that my family and I value — that have marked me as different, or as an outsider.”

Students interested in applying to the P.D. Soros fellowship should contact Kim Benard, assistant dean of distinguished fellowships in Career Advising and Professional Development.

Aziza Almanakly

Aziza Almanakly, a PhD student in electrical engineering and computer science, researches microwave quantum optics with superconducting qubits for quantum communication under Professor William Oliver in the Department of Physics. Almanakly’s career goal is to engineer multi-qubit systems that push boundaries in quantum technology.

Born and raised in northern New Jersey, Almanakly is the daughter of Syrian immigrants who came to the United States in the early 1990s in pursuit of academic opportunities. As the civil war in Syria grew dire, more of her relatives sought asylum in the U.S. Almanakly grew up around extended family who built a new version of their Syrian home in New Jersey.

Following in the footsteps of her mathematically minded father, Almanakly studied electrical engineering at The Cooper Union for the Advancement of Science and Art. She also pursued research opportunities in experimental quantum computing at Princeton University, the City University of New York, New York University, and Caltech.

Almanakly recognizes the importance of strong mentorship in diversifying engineering. She uses her unique experience as a New American and female engineer to encourage students from underrepresented backgrounds to enter STEM fields.

Alaleh Azhir

Alaleh Azhir grew up in Iran, where she pursued her passion for mathematics. She immigrated with her mother to the United States at age 14. Determined to overcome strict gender roles she had witnessed for women, Azhir is dedicated to improving health care for them.

Azhir graduated from Johns Hopkins University in 2019 with a perfect GPA as a triple major in biomedical engineering, computer science, and applied mathematics and statistics. A Rhodes and Barry Goldwater Scholar, she has developed many novel tools for visualization and analysis of genomics data at Johns Hopkins University, Harvard University, MIT, the National Institutes of Health, and laboratories in Switzerland.

After completing a master’s in statistical science at Oxford University, Azhir began her MD studies in the Harvard-MIT Program in Health Sciences and Technology. Her thesis focuses on the role of X and Y sex chromosomes on disease manifestations. Through medical training, she aims to build further computational tools specifically for preventive care for women. She has also founded and directs the nonprofit organization, Frappa, aimed at mentoring women living in Iran and helping them to immigrate abroad through the graduate school application process.

Brian Y. Chang PhD ’18

Born in Johnson City, New York, Brian Y. Chang PhD ’18 is the son of immigrants from the Shanghai municipality and Shandong Province in China. He pursued undergraduate and master’s degrees in mechanical engineering at Carnegie Mellon University, graduating in a combined four years with honors.

In 2018, Chang completed a PhD in medical engineering at MIT. Under the mentorship of Professor Elazer Edelman, Chang developed methods that make advanced cardiac technologies more accessible. The resulting approaches are used in hospitals around the world. Chang has published extensively and holds five patents.

With the goal of harnessing the power of engineering to improve patient care, Chang co-founded X-COR Therapeutics, a seed-funded medical device startup developing a more accessible treatment for lung failure with the potential to support patients with severe Covid-19 and chronic obstructive pulmonary disease.

After spending time in the hospital connecting with patients and teaching cardiovascular pathophysiology to medical students, Chang decided to attend medical school. He is currently a medical student in the Harvard-MIT Program in Health Sciences and Technology. Chang hopes to advance health care through medical device innovation and education as a future physician-scientist, entrepreneur, and educator.

Fiona Chen

MIT senior Fiona Chen was born in Cedar Park, Texas, the daughter of immigrants from China. Witnessing how her own and many other immigrant families faced significant difficulties finding work and financial stability sparked her interest in learning about poverty and economic inequality.

At MIT, Chen has pursued degrees in economics and mathematics. Her economics research projects have examined important policy issues — social isolation among students, global development and poverty, universal health-care systems, and the role of technology in shaping the labor market.

An active member of the MIT community, Chen has served as the officer on governance and officer on policy of the Undergraduate Association, MIT’s student government; the opinion editor of The Tech student newspaper; the undergraduate representative of several Institute-wide committees, including MIT’s Corporation Joint Advisory Committee; and one of the founding members of MIT Students Against War. In each of these roles, she has worked to advocate for policies to support underrepresented groups at MIT.

As a Soros fellow, Chen will pursue a PhD in economics to deepen her understanding of economic policy. Her ultimate goal is to become a professor who researches poverty and economic inequality, and applies her findings to craft policy solutions.

James Diao

James Diao graduated from Yale University with degrees in statistics and biochemistry and is currently a medical student at the Harvard-MIT Program in Health Sciences and Technology. He aspires to give voice to patient perspectives in the development and evaluation of health-care technology.

Diao grew up in Houston’s Chinatown, and spent summers with his extended family in Jiangxian. Diao’s family later moved to Fort Bend, Texas, where he found a pediatric oncologist mentor who introduced him to the wonders of modern molecular biology.

Diao’s interests include the responsible development of technology. At Apple, he led projects to validate wearable health features in diverse populations; at PathAI, he built deep learning models to broaden access to pathologist services; at Yale, where he worked on standardizing analyses of exRNA biomarkers; and at Harvard, he studied the impacts of clinical guidelines on marginalized groups.

Diao’s lead author research in the New England Journal of Medicine and JAMA systematically compared race-based and race-free equations for kidney function, and demonstrated that up to 1 million Black Americans may receive unequal kidney care due to their race. He has also published articles on machine learning and precision medicine.

Charlie ChangWon Lee

Born in Seoul, South Korea, Charlie ChangWon Lee was 10 when his family immigrated to the United States and settled in Palisades Park, New Jersey. The stress of his parents’ lack of health coverage ignited Lee’s determination to study the reasons for the high cost of health care in the U.S. and learn how to care for uninsured families like his own.

Lee graduated summa cum laude in integrative biology from Harvard College, winning the Hoopes Prize for his thesis on the therapeutic potential of human gut microbes. Lee’s research on novel therapies led him to question how newly approved, and expensive, medications could reach more patients.

At the Program on Regulation, Therapeutics, and Law (PORTAL) at Brigham and Women’s Hospital, Lee studied policy issues involving pharmaceutical drug pricing, drug development, and medication use and safety. His articles have appeared in JAMA, Health Affairs, and Mayo Clinic Proceedings.

As a first-year medical student at the Harvard-MIT Health Sciences and Technology program, Lee is investigating policies to incentivize vaccine and biosimilar drug development. He hopes to find avenues to bridge science and policy and translate medical innovations into accessible, affordable therapies.

Archana Podury

The daughter of Indian immigrants, Archana Podury was born in Mountain View, California. As an undergraduate at Cornell University, she studied the neural circuits underlying motor learning. Her growing interest in whole-brain dynamics led her to the Princeton Neuroscience Institute and Neuralink, where she discovered how brain-machine interfaces could be used to understand diffuse networks in the brain.

While studying neural circuits, Podury worked at a syringe exchange in Ithaca, New York, where she witnessed firsthand the mechanics of court-based drug rehabilitation. Now, as an MD student in the Harvard-MIT Health Sciences and Technology program, Podury is interested in combining computational and social approaches to neuropsychiatric disease.

In the Boyden Lab at the MIT McGovern Institute for Brain Research, Podury is developing human brain organoid models to better characterize circuit dysfunction in neurodevelopmental disorders. Concurrently, her work in the Dhand Lab at Brigham and Women’s Hospital applies network science tools to understand how patients’ social environments influence their health outcomes following acute neurological injury.

Podury hopes that focusing on both neural and social networks can lead toward a more comprehensive, and compassionate, approach to health and disease.

Ashwin Sah ’20

Ashwin Sah ’20 was born and raised in Portland, Oregon, the son of Indian immigrants. He developed a passion for mathematics research as an undergraduate at MIT, where he conducted research under Professor Yufei Zhao, as well as at the Duluth and Emory REU (Research Experience for Undergraduates) programs.

Sah has given talks on his work at multiple professional venues. His undergraduate research in varied areas of combinatorics and discrete mathematics culminated in the Barry Goldwater Scholarship and the Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student. Additionally, his work on diagonal Ramsey numbers was recently featured in Quanta Magazine.

Beyond research, Sah has pursued opportunities to give back to the math community, helping to organize or grade competitions such as the Harvard-MIT Mathematics Tournament and the USA Mathematical Olympiad. He has also been a grader at the Mathematical Olympiad Program, a camp for talented high-school students in the United States, and an instructor for the Monsoon Math Camp, a virtual program aimed at teaching higher mathematics to high school students in India.

Sah is currently a PhD student in mathematics at MIT, where he continues to work with Zhao.

Enrique Toloza

Enrique Toloza was born in Los Angeles, California, the child of two immigrants: one from Colombia who came to the United States for a PhD and the other from the Philippines who grew up in California and went on to medical school. Their literal marriage of science and medicine inspired Toloza to become a physician-scientist.

Toloza majored in physics and Spanish literature at the University of North Carolina at Chapel Hill. He eventually settled on an interest in theoretical neuroscience after a summer research internship at MIT and completing an honors thesis on noninvasive brain stimulation.

After college, Toloza joined Professor Mark Harnett’s laboratory at MIT for a year. He went on to enroll in the Harvard-MIT MD/PhD program, studying within the Health Sciences and Technology MD curriculum at Harvard and the PhD program at MIT. For his PhD, Toloza rejoined Harnett to conduct research on the biophysics of dendritic integration and the contribution of dendrites to cortical computations in the brain.

Toloza is passionate about expanding health care access to immigrant populations. In college, he led the interpreting team at the University of North Carolina at Chapel Hill’s student-run health clinic; at Harvard Medical School, he has worked with Spanish-speaking patients as a student clinician.

How the brain encodes landmarks that help us navigate

When we move through the streets of our neighborhood, we often use familiar landmarks to help us navigate. And as we think to ourselves, “OK, now make a left at the coffee shop,” a part of the brain called the retrosplenial cortex (RSC) lights up.

While many studies have linked this brain region with landmark-based navigation, exactly how it helps us find our way is not well-understood. A new study from MIT neuroscientists now reveals how neurons in the RSC use both visual and spatial information to encode specific landmarks.

“There’s a synthesis of some of these signals — visual inputs and body motion — to represent concepts like landmarks,” says Mark Harnett, an assistant professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “What we went after in this study is the neuron-level and population-level representation of these different aspects of spatial navigation.”

In a study of mice, the researchers found that this brain region creates a “landmark code” by combining visual information about the surrounding environment with spatial feedback of the mice’s own position along a track. Integrating these two sources of information allowed the mice to learn where to find a reward, based on landmarks that they saw.

“We believe that this code that we found, which is really locked to the landmarks, and also gives the animals a way to discriminate between landmarks, contributes to the animals’ ability to use those landmarks to find rewards,” says Lukas Fischer, an MIT postdoc and the lead author of the study.

Harnett is the senior author of the study, which appears today in the journal eLife. Other authors are graduate student Raul Mojica Soto-Albors and recent MIT graduate Friederike Buck.

Encoding landmarks

Previous studies have found that people with damage to the RSC have trouble finding their way from one place to another, even though they can still recognize their surroundings. The RSC is also one of the first areas affected in Alzheimer’s patients, who often have trouble navigating.

The RSC is wedged between the primary visual cortex and the motor cortex, and it receives input from both of those areas. It also appears to be involved in combining two types of representations of space — allocentric, meaning the relationship of objects to each other, and egocentric, meaning the relationship of objects to the viewer.

“The evidence suggests that RSC is really a place where you have a fusion of these different frames of reference,” Harnett says. “Things look different when I move around in the room, but that’s because my vantage point has changed. They’re not changing with respect to one another.”

In this study, the MIT team set out to analyze the behavior of individual RSC neurons in mice, including how they integrate multiple inputs that help with navigation. To do that, they created a virtual reality environment for the mice by allowing them to run on a treadmill while they watch a video screen that makes it appear they are running along a track. The speed of the video is determined by how fast the mice run.

At specific points along the track, landmarks appear, signaling that there’s a reward available a certain distance beyond the landmark. The mice had to learn to distinguish between two different landmarks, and to learn how far beyond each one they had to run to get the reward.

Once the mice learned the task, the researchers recorded neural activity in the RSC as the animals ran along the virtual track. They were able to record from a few hundred neurons at a time, and found that most of them anchored their activity to a specific aspect of the task.

There were three primary anchoring points: the beginning of the trial, the landmark, and the reward point. The majority of the neurons were anchored to the landmarks, meaning that their activity would consistently peak at a specific point relative to the landmark, say 50 centimeters before it or 20 centimeters after it.

Most of those neurons responded to both of the landmarks, but a small subset responded to only one or the other. The researchers hypothesize that those strongly selective neurons help the mice to distinguish between the landmarks and run the correct distance to get the reward.

When the researchers used optogenetics (a tool that can turn off neuron activity) to block activity in the RSC, the mice’s performance on the task became much worse.

Combining inputs

The researchers also did an experiment in which the mice could choose to run or not while the video played at a constant speed, unrelated to the mice’s movement. The mice could still see the landmarks, but the location of the landmarks was no longer linked to a reward or to the animals’ own behavior. In that situation, RSC neurons did respond to the landmarks, but not as strongly as they did when the mice were using them for navigation.

Further experiments allowed the researchers to tease out just how much neuron activation is produced by visual input (seeing the landmarks) and by feedback on the mouse’s own movement. However, simply adding those two numbers yielded totals much lower than the neuron activity seen when the mice were actively navigating the track.

“We believe that is evidence for a mechanism of nonlinear integration of these inputs, where they get combined in a way that creates a larger response than what you would get if you just added up those two inputs in a linear fashion,” Fischer says.

The researchers now plan to analyze data that they have already collected on how neuron activity evolves over time as the mice learn the task. They also hope to perform further experiments in which they could try to separately measure visual and spatial inputs into different locations within RSC neurons.

The research was funded by the National Institutes of Health, the McGovern Institute, the NEC Corporation Fund for Research in Computers and Communications at MIT, and the Klingenstein-Simons Fellowship in Neuroscience.

Single neurons can encode distinct landmarks

The organization of many neurons wired together in a complex circuit gives the brain its ability to perform powerful calculations. Work from the Harnett lab recently showed that even single neurons can process more information than previously thought, representing distinct variables at the subcellular level during behavior.

McGovern Investigator Mark Harnett and postdoc Jakob Voigts conducted an extremely delicate and intricate imaging experiment on different parts of the same neuron in the mouse retinosplenial cortex during 2-D navigation. Their set up allowed 2-photon imaging of neuronal sub-compartments during free 2-D navigation with head rotation, the latter being important to follow neural activity during naturalistic, complex behavior.

Recording computation by subcompartments in neurons.


In the work, published recently in Neuron, the authors used Ca2+-imaging to show that the soma in a single neuron was consistently active when mice were at particular landmarks as they navigated in an arena. The dendrites (tree-like antennas that receive input from other neurons) of exactly the same neuron were robustly active independent of the soma at distinct positions and orientations in the arena. This strongly suggests that the dendrites encode distinct information compared to their parent soma, in this case spatial variables during navigation, laying the foundation for studying sub-cellular processes during complex behaviors.


Mark Harnett receives a 2019 McKnight Scholar Award

McGovern Institute investigator Mark Harnett is one of six young researchers selected to receive a prestigious 2019 McKnight Scholar Award. The award supports his research “studying how dendrites, the antenna-like input structures of neurons, contribute to computation in neural networks.”

Harnett examines the biophysical properties of single neurons, ultimately aiming to understand how these relate to the complex computations that underlie behavior. His lab was the first to examine the biophysical properties of human dendrites. The Harnett lab found that human neurons have distinct properties, including increased dendritic compartmentalization that could allow more complex computations within single neurons. His lab recently discovered that such dendritic computations are not rare, or confined to specific behaviors, but are a widespread and general feature of neuronal activity.

“As a young investigator, it is hard to prioritize so many exciting directions and ideas,” explains Harnett. “I really want to thank the McKnight Foundation, both for the support, but also for the hard work the award committee puts into carefully thinking about and giving feedback on proposals. It means a lot to get this type of endorsement from a seriously committed and distinguished committee, and their support gives even stronger impetus to pursue this research direction.”

The McKnight Foundation has supported neuroscience research since 1977, and provides three prominent awards, with the Scholar award aimed at supporting young scientists, and drawing applications from the strongest young neuroscience faculty across the US. William L. McKnight (1887-1979) was an early leader of the 3M Company and had a personal interest in memory and brain diseases. The McKnight Foundation was established with this focus in mind, and the Scholar Award provides $75,000 per year for three years to support cutting edge neuroscience research.


Antenna-like inputs unexpectedly active in neural computation

Most neurons have many branching extensions called dendrites that receive input from thousands of other neurons. Dendrites aren’t just passive information-carriers, however. According to a new study from MIT, they appear to play a surprisingly large role in neurons’ ability to translate incoming signals into electrical activity.

Neuroscientists had previously suspected that dendrites might be active only rarely, under specific circumstances, but the MIT team found that dendrites are nearly always active when the main cell body of the neuron is active.

“It seems like dendritic spikes are an intrinsic feature of how neurons in our brain can compute information. They’re not a rare event,” says Lou Beaulieu-Laroche, an MIT graduate student and the lead author of the study. “All the neurons that we looked at had these dendritic spikes, and they had dendritic spikes very frequently.”

The findings suggest that the role of dendrites in the brain’s computational ability is much larger than had previously been thought, says Mark Harnett, who is the Fred and Carole Middleton Career Development Assistant Professor of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and the senior author of the paper.

“It’s really quite different than how the field had been thinking about this,” he says. “This is evidence that dendrites are actively engaged in producing and shaping the outputs of neurons.”

Graduate student Enrique Toloza and technical associate Norma Brown are also authors of the paper, which appears in Neuron on June 6.

“A far-flung antenna”

Dendrites receive input from many other neurons and carry those signals to the cell body, also called the soma. If stimulated enough, a neuron fires an action potential — an electrical impulse that spreads to other neurons. Large networks of these neurons communicate with each other to perform complex cognitive tasks such as producing speech.

Through imaging and electrical recording, neuroscientists have learned a great deal about the anatomical and functional differences between different types of neurons in the brain’s cortex, but little is known about how they incorporate dendritic inputs and decide whether to fire an action potential. Dendrites give neurons their characteristic branching tree shape, and the size of the “dendritic arbor” far exceeds the size of the soma.

“It’s an enormous, far-flung antenna that’s listening to thousands of synaptic inputs distributed in space along that branching structure from all the other neurons in the network,” Harnett says.

Some neuroscientists have hypothesized that dendrites are active only rarely, while others thought it possible that dendrites play a more central role in neurons’ overall activity. Until now, it has been difficult to test which of these ideas is more accurate, Harnett says.

To explore dendrites’ role in neural computation, the MIT team used calcium imaging to simultaneously measure activity in both the soma and dendrites of individual neurons in the visual cortex of the brain. Calcium flows into neurons when they are electrically active, so this measurement allowed the researchers to compare the activity of dendrites and soma of the same neuron. The imaging was done while mice performed simple tasks such as running on a treadmill or watching a movie.

Unexpectedly, the researchers found that activity in the soma was highly correlated with dendrite activity. That is, when the soma of a particular neuron was active, the dendrites of that neuron were also active most of the time. This was particularly surprising because the animals weren’t performing any kind of cognitively demanding task, Harnett says.

“They weren’t engaged in a task where they had to really perform and call upon cognitive processes or memory. This is pretty simple, low-level processing, and already we have evidence for active dendritic processing in almost all the neurons,” he says. “We were really surprised to see that.”

Evolving patterns

The researchers don’t yet know precisely how dendritic input contributes to neurons’ overall activity, or what exactly the neurons they studied are doing.

“We know that some of those neurons respond to some visual stimuli, but we don’t necessarily know what those individual neurons are representing. All we can say is that whatever the neuron is representing, the dendrites are actively participating in that,” Beaulieu-Laroche says.

While more work remains to determine exactly how the activity in the dendrites and the soma are linked, “it is these tour-de-force in vivo measurements that are critical for explicitly testing hypotheses regarding electrical signaling in neurons,” says Marla Feller, a professor of neurobiology at the University of California at Berkeley, who was not involved in the research.

The MIT team now plans to investigate how dendritic activity contributes to overall neuronal function by manipulating dendrite activity and then measuring how it affects the activity of the cell body, Harnett says. They also plan to study whether the activity patterns they observed evolve as animals learn a new task.

“One hypothesis is that dendritic activity will actually sharpen up for representing features of a task you taught the animals, and all the other dendritic activity, and all the other somatic activity, is going to get dampened down in the rest of the cortical cells that are not involved,” Harnett says.

The research was funded by the Natural Sciences and Engineering Research Council of Canada and the U.S. National Institutes of Health.

Mark Harnett

Listening to Neurons

Mark Harnett studies how the biophysical features of individual neurons, including ion channels, receptors, and membrane electrical properties, endow neural circuits with the ability to process information and perform the complex computations that underlie behavior. As part of this work, the Harnett lab was the first to describe the physiological properties of human dendrites, the elaborate tree-like structures through which neurons receive the vast majority of their synaptic inputs. Harnett also examines how computations are instantiated in neural circuits to produce complex behaviors such as spatial navigation.