Author: MIT News

Dendrites may help neurons perform complicated calculations

Anne Trafton |

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

by MIT School of Science |

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.

Where did that sound come from?

Anne Trafton |

The human brain is finely tuned not only to recognize particular sounds, but also to determine which direction they came from. By comparing differences in sounds that reach the right and left ear, the brain can estimate the location of a barking dog, wailing fire engine, or approaching car.

MIT neuroscientists have now developed a computer model that can also perform that complex task. The model, which consists of several convolutional neural networks, not only performs the task as well as humans do, it also struggles in the same ways that humans do.

“We now have a model that can actually localize sounds in the real world,” says Josh McDermott, an associate professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “And when we treated the model like a human experimental participant and simulated this large set of experiments that people had tested humans on in the past, what we found over and over again is it the model recapitulates the results that you see in humans.”

Findings from the new study also suggest that humans’ ability to perceive location is adapted to the specific challenges of our environment, says McDermott, who is also a member of MIT’s Center for Brains, Minds, and Machines.

McDermott is the senior author of the paper, which appears today in Nature Human Behavior. The paper’s lead author is MIT graduate student Andrew Francl.

Modeling localization

When we hear a sound such as a train whistle, the sound waves reach our right and left ears at slightly different times and intensities, depending on what direction the sound is coming from. Parts of the midbrain are specialized to compare these slight differences to help estimate what direction the sound came from, a task also known as localization.

This task becomes markedly more difficult under real-world conditions — where the environment produces echoes and many sounds are heard at once.

Scientists have long sought to build computer models that can perform the same kind of calculations that the brain uses to localize sounds. These models sometimes work well in idealized settings with no background noise, but never in real-world environments, with their noises and echoes.

To develop a more sophisticated model of localization, the MIT team turned to convolutional neural networks. This kind of computer modeling has been used extensively to model the human visual system, and more recently, McDermott and other scientists have begun applying it to audition as well.

Convolutional neural networks can be designed with many different architectures, so to help them find the ones that would work best for localization, the MIT team used a supercomputer that allowed them to train and test about 1,500 different models. That search identified 10 that seemed the best-suited for localization, which the researchers further trained and used for all of their subsequent studies.

To train the models, the researchers created a virtual world in which they can control the size of the room and the reflection properties of the walls of the room. All of the sounds fed to the models originated from somewhere in one of these virtual rooms. The set of more than 400 training sounds included human voices, animal sounds, machine sounds such as car engines, and natural sounds such as thunder.

The researchers also ensured the model started with the same information provided by human ears. The outer ear, or pinna, has many folds that reflect sound, altering the frequencies that enter the ear, and these reflections vary depending on where the sound comes from. The researchers simulated this effect by running each sound through a specialized mathematical function before it went into the computer model.

“This allows us to give the model the same kind of information that a person would have,” Francl says.

After training the models, the researchers tested them in a real-world environment. They placed a mannequin with microphones in its ears in an actual room and played sounds from different directions, then fed those recordings into the models. The models performed very similarly to humans when asked to localize these sounds.

“Although the model was trained in a virtual world, when we evaluated it, it could localize sounds in the real world,” Francl says.

Similar patterns

The researchers then subjected the models to a series of tests that scientists have used in the past to study humans’ localization abilities.

In addition to analyzing the difference in arrival time at the right and left ears, the human brain also bases its location judgments on differences in the intensity of sound that reaches each ear. Previous studies have shown that the success of both of these strategies varies depending on the frequency of the incoming sound. In the new study, the MIT team found that the models showed this same pattern of sensitivity to frequency.

“The model seems to use timing and level differences between the two ears in the same way that people do, in a way that’s frequency-dependent,” McDermott says.

The researchers also showed that when they made localization tasks more difficult, by adding multiple sound sources played at the same time, the computer models’ performance declined in a way that closely mimicked human failure patterns under the same circumstances.

“As you add more and more sources, you get a specific pattern of decline in humans’ ability to accurately judge the number of sources present, and their ability to localize those sources,” Francl says. “Humans seem to be limited to localizing about three sources at once, and when we ran the same test on the model, we saw a really similar pattern of behavior.”

Because the researchers used a virtual world to train their models, they were also able to explore what happens when their model learned to localize in different types of unnatural conditions. The researchers trained one set of models in a virtual world with no echoes, and another in a world where there was never more than one sound heard at a time. In a third, the models were only exposed to sounds with narrow frequency ranges, instead of naturally occurring sounds.

When the models trained in these unnatural worlds were evaluated on the same battery of behavioral tests, the models deviated from human behavior, and the ways in which they failed varied depending on the type of environment they had been trained in. These results support the idea that the localization abilities of the human brain are adapted to the environments in which humans evolved, the researchers say.

The researchers are now applying this type of modeling to other aspects of audition, such as pitch perception and speech recognition, and believe it could also be used to understand other cognitive phenomena, such as the limits on what a person can pay attention to or remember, McDermott says.

The research was funded by the National Science Foundation and the National Institute on Deafness and Other Communication Disorders.

Five MIT faculty elected 2021 AAAS Fellows

by MIT News Office |

Five MIT faculty members have been elected as fellows of the American Association for the Advancement of Science (AAAS).

The 2021 class of AAAS Fellows includes 564 scientists, engineers, and innovators spanning 24 scientific disciplines who are being recognized for their scientifically and socially distinguished achievements.

Mircea Dincă is the W. M. Keck Professor of Energy in the Department of Chemistry. His group’s research focuses on addressing challenges related to the storage and consumption of energy, and global environmental concerns. Central to these efforts are the synthesis of novel organic-inorganic hybrid materials and the manipulation of their electrochemical and photophysical properties, with a current emphasis on porous materials and extended one-dimensional van der Waals materials.

Guoping Feng is the James W. and Patricia T. Poitras Professor of Neuroscience in the Department of Brain and Cognitive Sciences, associate director of MIT’s McGovern Institute for Brain Research, director of Model Systems and Neurobiology at the Stanley Center for Psychiatric Research, and an institute member of the Broad Institute of MIT and Harvard. His research is devoted to understanding the development and function of synapses in the brain and how synaptic dysfunction may contribute to neurodevelopmental and psychiatric disorders. By understanding the molecular, cellular, and circuitry mechanisms of these disorders, Feng hopes his work will eventually lead to the development of new and effective treatments for the millions of people suffering from these devastating diseases.

David Shoemaker is a senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research. His work is focused on gravitational-wave observation and includes developing technologies for the detectors (LIGO, LISA), developing proposals for new instruments (Cosmic Explorer), managing the teams to build them and the consortia which exploit the data (LIGO Scientific Collaboration, LISA Consortium), and supporting the overall growth of the field (Gravitational-Wave International Committee).

Ian Hunter is the Hatsopoulos Professor of Mechanical Engineering and runs the Bioinstrumentation Lab at MIT. His main areas of research are instrumentation, microrobotics, medical devices, and biomimetic materials. Over the years he and his students have developed many instruments and devices including: confocal laser microscopes, scanning tunneling electron microscopes, miniature mass spectrometers, new forms of Raman spectroscopy, needle-free drug delivery technologies, nano- and micro-robots, microsurgical robots, robotic endoscopes, high-performance Lorentz force motors, and microarray technologies for massively parallel chemical and biological assays.

Evelyn N. Wang is the Ford Professor of Engineering and head of the Department of Mechanical Engineering. Her research program combines fundamental studies of micro/nanoscale heat and mass transport processes with the development of novel engineered structures to create innovative solutions in thermal management, energy, and water harvesting systems. Her work in thermophotovoltaics was named to Technology Review’s lists of Biggest Clean Energy Advances, in 2016, and Ten Breakthrough Technologies, in 2017, and to the Department of Energy Frontiers Research Center’s Ten of Ten awards. Her work extracting water from air has won her the title of 2017 Foreign Policy’s Global ReThinker and the 2018 Eighth Prince Sultan bin Abdulaziz International Prize for Water.

Babies can tell who has close relationships based on one clue: saliva

Anne Trafton |

Learning to navigate social relationships is a skill that is critical for surviving in human societies. For babies and young children, that means learning who they can count on to take care of them.

MIT neuroscientists have now identified a specific signal that young children and even babies use to determine whether two people have a strong relationship and a mutual obligation to help each other: whether those two people kiss, share food, or have other interactions that involve sharing saliva.

In a new study, the researchers showed that babies expect people who share saliva to come to one another’s aid when one person is in distress, much more so than when people share toys or interact in other ways that do not involve saliva exchange. The findings suggest that babies can use these cues to try to figure out who around them is most likely to offer help, the researchers say.

“Babies don’t know in advance which relationships are the close and morally obligating ones, so they have to have some way of learning this by looking at what happens around them,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

MIT postdoc Ashley Thomas is the lead author of the study, which appears today in Science. Brandon Woo, a Harvard University graduate student; Daniel Nettle, a professor of behavioral science at Newcastle University; and Elizabeth Spelke, a professor of psychology at Harvard, are also authors of the paper.

Sharing saliva

In human societies, people typically distinguish between “thick” and “thin” relationships. Thick relationships, usually found between family members, feature strong levels of attachment, obligation, and mutual responsiveness. Anthropologists have also observed that people in thick relationships are more willing to share bodily fluids such as saliva.

“That inspired both the question of whether infants distinguish between those types of relationships, and whether saliva sharing might be a really good cue they could use to recognize them,” Thomas says.

To study those questions, the researchers observed toddlers (16.5 to 18.5 months) and babies (8.5 to 10 months) as they watched interactions between human actors and puppets. In the first set of experiments, a puppet shared an orange with one actor, then tossed a ball back and forth with a different actor.

After the children watched these initial interactions, the researchers observed the children’s reactions when the puppet showed distress while sitting between the two actors. Based on an earlier study of nonhuman primates, the researchers hypothesized that babies would look first at the person whom they expected to help. That study showed that when baby monkeys cry, other members of the troop look to the baby’s parents, as if expecting them to step in.

The MIT team found that the children were more likely to look toward the actor who had shared food with the puppet, not the one who had shared a toy, when the puppet was in distress.

In a second set of experiments, designed to focus more specifically on saliva, the actor either placed her finger in her mouth and then into the mouth of the puppet, or placed her finger on her forehead and then onto the forehead of the puppet. Later, when the actor expressed distress while standing between the two puppets, children watching the video were more likely to look toward the puppet with whom she had shared saliva.

Social cues

The findings suggest that saliva sharing is likely an important cue that helps infants to learn about their own social relationships and those of people around them, the researchers say.

“The general skill of learning about social relationships is very useful,” Thomas says. “One reason why this distinction between thick and thin might be important for infants in particular, especially human infants, who depend on adults for longer than many other species, is that it might be a good way to figure out who else can provide the support that they depend on to survive.”

The researchers did their first set of studies shortly before Covid-19 lockdowns began, with babies who came to the lab with their families. Later experiments were done over Zoom. The results that the researchers saw were similar before and after the pandemic, confirming that pandemic-related hygiene concerns did not affect the outcome.

“We actually know the results would have been similar if it hadn’t been for the pandemic,” Saxe says. “You might wonder, did kids start to think very differently about sharing saliva when suddenly everybody was talking about hygiene all the time? So, for that question, it’s very useful that we had an initial data set collected before the pandemic.”

Doing the second set of studies on Zoom also allowed the researchers to recruit a much more diverse group of children because the subjects were not limited to families who could come to the lab in Cambridge during normal working hours.

In future work, the researchers hope to perform similar studies with infants in cultures that have different types of family structures. In adult subjects, they plan to use functional magnetic resonance imaging (fMRI) to study what parts of the brain are involved in making saliva-based assessments about social relationships.

The research was funded by the National Institutes of Health; the Patrick J. McGovern Foundation; the Guggenheim Foundation; a Social Sciences and Humanities Research Council Doctoral Fellowship; MIT’s Center for Brains, Minds, and Machines; and the Siegel Foundation.

MIT Future Founders Initiative announces prize competition to promote female entrepreneurs in biotech

Anne Trafton |

In a fitting sequel to its entrepreneurship “boot camp” educational lecture series last fall, the MIT Future Founders Initiative has announced the MIT Future Founders Prize Competition, supported by Northpond Ventures, and named the MIT faculty cohort that will participate in this year’s competition. The Future Founders Initiative was established in 2020 to promote female entrepreneurship in biotech.

Despite increasing representation at MIT, female science and engineering faculty found biotech startups at a disproportionately low rate compared with their male colleagues, according to research led by the initiative’s founders, MIT Professor Sangeeta Bhatia, MIT Professor and President Emerita Susan Hockfield, and MIT Amgen Professor of Biology Emerita Nancy Hopkins. In addition to highlighting systemic gender imbalances in the biotech pipeline, the initiative’s founders emphasize that the dearth of female biotech entrepreneurs represents lost opportunities for society as a whole — a bottleneck in the proliferation of publicly accessible medical and technological innovation.

“A very common myth is that representation of women in the pipeline is getting better with time … We can now look at the data … and simply say, ‘that’s not true’,” said Bhatia, who is the John and Dorothy Wilson Professor of Health Sciences and Technology and Electrical Engineering and Computer Science, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science, in an interview for the March/April 2021 MIT Faculty Newsletter. “We need new solutions. This isn’t just about waiting and being optimistic.”

Inspired by generous funding from Northpond Labs, the research and development-focused affiliate of Northpond Ventures, and by the success of other MIT prize incentive competitions such as the Climate Tech and Energy Prize, the Future Founders Initiative Prize Competition will be structured as a learning cohort in which participants will be supported in commercializing their existing inventions with instruction in market assessments, fundraising, and business capitalization, as well as other programming. The program, which is being run as a partnership between the MIT School of Engineering and the Martin Trust Center for MIT Entrepreneurship, provides hands-on opportunities to learn from industry leaders about their experiences, ranging from licensing technology to creating early startup companies. Bhatia and Kit Hickey, an entrepreneur-in-residence at the Martin Trust Center and senior lecturer at the MIT Sloan School of Management, are co-directors of the program.

“The competition is an extraordinary effort to increase the number of female faculty who translate their research and ideas into real-world applications through entrepreneurship,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Our hope is that this likewise serves as an opportunity for participants to gain exposure and experience to the many ways in which they could achieve commercial impact through their research.”

At the end of the program, the cohort members will pitch their ideas to a selection committee composed of MIT faculty, biotech founders, and venture capitalists. The grand prize winner will receive $250,000 in discretionary funds, and two runners-up will receive $100,000. The winners will be announced at a showcase event, at which the entire cohort will present their work. All participants will also receive a $10,000 stipend for participating in the competition.

“The biggest payoff is not identifying the winner of the competition,” says Bhatia. “Really, what we are doing is creating a cohort … and then, at the end, we want to create a lot of visibility for these women and make them ‘top of mind’ in the community.”

The Selection Committee members for the MIT Future Founders Prize Competition are:

  • Bill Aulet, professor of the practice in the MIT Sloan School of Management and managing director of the Martin Trust Center for MIT Entrepreneurship
  • Sangeeta Bhatia, the John and Dorothy Wilson Professor of Electrical Engineering and Computer Science at MIT; a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science; and founder of Hepregen, Glympse Bio, and Satellite Bio
  • Kit Hickey, senior lecturer in the MIT Sloan School of Management and entrepreneur-in-residence at the Martin Trust Center
  • Susan Hockfield, MIT president emerita and professor of neuroscience
  • Andrea Jackson, director at Northpond Ventures
  • Harvey Lodish, professor of biology and biomedical engineering at MIT and founder of Genzyme, Millennium, and Rubius
  • Fiona Murray, associate dean for innovation and inclusion in the MIT Sloan School of Management; the William Porter Professor of Entrepreneurship; co-director of the MIT Innovation Initiative; and faculty director of the MIT Legatum Center
  • Amy Schulman, founding CEO of Lyndra Therapeutics and partner at Polaris Partners
  • Nandita Shangari, managing director at Novartis Venture Fund

“As an investment firm dedicated to supporting entrepreneurs, we are acutely aware of the limited number of companies founded and led by women in academia. We believe humanity should be benefiting from brilliant ideas and scientific breakthroughs from women in science, which could address many of the world’s most pressing problems. Together with MIT, we are providing an opportunity for women faculty members to enhance their visibility and gain access to the venture capital ecosystem,” says Andrea Jackson, director at Northpond Ventures.

“This first cohort is representative of the unrealized opportunity this program is designed to capture. While it will take a while to build a robust community of connections and role models, I am pleased and confident this program will make entrepreneurship more accessible and inclusive to our community, which will greatly benefit society,” says Susan Hockfield, MIT president emerita.

The MIT Future Founders Prize Competition cohort members were selected from schools across MIT, including the School of Science, the School of Engineering, and Media Lab within the School of Architecture and Planning. They are:

Polina Anikeeva is professor of materials science and engineering and brain and cognitive sciences, an associate member of the McGovern Institute for Brain Research, and the associate director of the Research Laboratory of Electronics. She is particularly interested in advancing the possibility of future neuroprosthetics, through biologically-informed materials synthesis, modeling, and device fabrication. Anikeeva earned her BS in biophysics from St. Petersburg State Polytechnic University and her PhD in materials science and engineering from MIT.

Natalie Artzi is principal research scientist in the Institute of Medical Engineering and Science and an assistant professor in the department of medicine at Brigham and Women’s Hospital. Through the development of smart materials and medical devices, her research seeks to “personalize” medical interventions based on the specific presentation of diseased tissue in a given patient. She earned both her BS and PhD in chemical engineering from the Technion-Israel Institute of Technology.

Laurie A. Boyer is professor of biology and biological engineering in the Department of Biology. By studying how diverse molecular programs cross-talk to regulate the developing heart, she seeks to develop new therapies that can help repair cardiac tissue. She earned her BS in biomedical science from Framingham State University and her PhD from the University of Massachusetts Medical School.

Tal Cohen is associate professor in the departments of Civil and Environmental Engineering and Mechanical Engineering. She wields her understanding of how materials behave when they are pushed to their extremes to tackle engineering challenges in medicine and industry. She earned her BS, MS, and PhD in aerospace engineering from the Technion-Israel Institute of Technology.

Canan Dagdeviren is assistant professor of media arts and sciences and the LG Career Development Professor of Media Arts and Sciences. Her research focus is on creating new sensing, energy harvesting, and actuation devices that can be stretched, wrapped, folded, twisted, and implanted onto the human body while maintaining optimal performance. She earned her BS in physics engineering from Hacettepe University, her MS in materials science and engineering from Sabanci University, and her PhD in materials science and engineering from the University of Illinois at Urbana-Champaign.

Ariel Furst is the Raymond (1921) & Helen St. Laurent Career Development Professor in the Department of Chemical Engineering. Her research addresses challenges in global health and sustainability, utilizing electrochemical methods and biomaterials engineering. She is particularly interested in new technologies that detect and treat disease. Furst earned her BS in chemistry at the University of Chicago and her PhD at Caltech.

Kristin Knouse is assistant professor in the Department of Biology and the Koch Institute for Integrative Cancer Research. She develops tools to investigate the molecular regulation of organ injury and regeneration directly within a living organism with the goal of uncovering novel therapeutic avenues for diverse diseases. She earned her BS in biology from Duke University, her PhD and MD through the Harvard and MIT MD-PhD program.

Elly Nedivi is the William R. (1964) & Linda R. Young Professor of Neuroscience at the Picower Institute for Learning and Memory with joint appointments in the departments of Brain and Cognitive Sciences and Biology. Through her research of neurons, genes, and proteins, Nedivi focuses on elucidating the cellular mechanisms that control plasticity in both the developing and adult brain. She earned her BS in biology from Hebrew University and her PhD in neuroscience from Stanford University.

Ellen Roche is associate professor in the Department of Mechanical Engineering and Institute of Medical Engineering and Science, and the W.M. Keck Career Development Professor in Biomedical Engineering. Borrowing principles and design forms she observes in nature, Roche works to develop implantable therapeutic devices that assist cardiac and other biological function. She earned her bachelor’s degree in biomedical engineering from the National University of Ireland at Galway, her MS in bioengineering from Trinity College Dublin, and her PhD from Harvard University.

A key brain region responds to faces similarly in infants and adults

Anne Trafton |

Within the visual cortex of the adult brain, a small region is specialized to respond to faces, while nearby regions show strong preferences for bodies or for scenes such as landscapes.

Neuroscientists have long hypothesized that it takes many years of visual experience for these areas to develop in children. However, a new MIT study suggests that these regions form much earlier than previously thought. In a study of babies ranging in age from two to nine months, the researchers identified areas of the infant visual cortex that already show strong preferences for either faces, bodies, or scenes, just as they do in adults.

“These data push our picture of development, making babies’ brains look more similar to adults, in more ways, and earlier than we thought,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

Using functional magnetic resonance imaging (fMRI), the researchers collected usable data from more than 50 infants, a far greater number than any research lab has been able to scan before. This allowed them to examine the infant visual cortex in a way that had not been possible until now.

“This is a result that’s going to make a lot of people have to really grapple with their understanding of the infant brain, the starting point of development, and development itself,” says Heather Kosakowski, an MIT graduate student and the lead author of the study, which appears today in Current Biology.

MIT graduate student Heather Kosakowski prepares an infant for an MRI scan at the Martinos Imaging Center. Photo: Caitlin Cunningham

Distinctive regions

More than 20 years ago, Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, used fMRI to discover the fusiform face area: a small region of the visual cortex that responds much more strongly to faces than any other kind of visual input.

Since then, Kanwisher and her colleagues have also identified parts of the visual cortex that respond to bodies (the extrastriate body area, or EBA), and scenes (the parahippocampal place area, or PPA).

“There is this set of functionally very distinctive regions that are present in more or less the same place in pretty much every adult,” says Kanwisher, who is also a member of MIT’s Center for Brains, Minds, and Machines, and an author of the new study. “That raises all these questions about how these regions develop. How do they get there, and how do you build a brain that has such similar structure in each person?”

One way to try to answer those questions is to investigate when these highly selective regions first develop in the brain. A longstanding hypothesis is that it takes several years of visual experience for these regions to gradually become selective for their specific targets. Scientists who study the visual cortex have found similar selectivity patterns in children as young as 4 or 5 years old, but there have been few studies of children younger than that.

In 2017, Saxe and one of her graduate students, Ben Deen, reported the first successful use of fMRI to study the brains of awake infants. That study, which included data from nine babies, suggested that while infants did have areas that respond to faces and scenes, those regions were not yet highly selective. For example, the fusiform face area did not show a strong preference for human faces over every other kind of input, including human bodies or the faces of other animals.

However, that study was limited by the small number of subjects, and also by its reliance on an fMRI coil that the researchers had developed especially for babies, which did not offer as high-resolution imaging as the coils used for adults.

For the new study, the researchers wanted to try to get better data, from more babies. They built a new scanner that is more comfortable for babies and also more powerful, with resolution similar to that of fMRI scanners used to study the adult brain.

After going into the specialized scanner, along with a parent, the babies watched videos that showed either faces, body parts such as kicking feet or waving hands, objects such as toys, or natural scenes such as mountains.

The researchers recruited nearly 90 babies for the study, collected usable fMRI data from 52, half of which contributed higher-resolution data collected using the new coil. Their analysis revealed that specific regions of the infant visual cortex show highly selective responses to faces, body parts, and natural scenes, in the same locations where those responses are seen in the adult brain. The selectivity for natural scenes, however, was not as strong as for faces or body parts.

The infant brain

The findings suggest that scientists’ conception of how the infant brain develops may need to be revised to accommodate the observation that these specialized regions start to resemble those of adults sooner than anyone had expected.

“The thing that is so exciting about these data is that they revolutionize the way we understand the infant brain,” Kosakowski says. “A lot of theories have grown up in the field of visual neuroscience to accommodate the view that you need years of development for these specialized regions to emerge. And what we’re saying is actually, no, you only really need a couple of months.”

Because their data on the area of the brain that responds to scenes was not as strong as for the other locations they looked at, the researchers now plan to pursue additional studies of that region, this time showing babies images on a much larger screen that will more closely mimic the experience of being within a scene. For that study, they plan to use near-infrared spectroscopy (NIRS), a non-invasive imaging technique that doesn’t require the participant to be inside a scanner.

“That will let us ask whether young babies have robust responses to visual scenes that we underestimated in this study because of the visual constraints of the experimental setup in the scanner,” Saxe says.

The researchers are now further analyzing the data they gathered for this study in hopes of learning more about how development of the fusiform face area progresses from the youngest babies they studied to the oldest. They also hope to perform new experiments examining other aspects of cognition, including how babies’ brains respond to language and music.

The research was funded by the National Science Foundation, the National Institutes of Health, the McGovern Institute, and the Center for Brains, Minds, and Machines.

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

Anne Trafton |

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.

Giving robots social skills

by Adam Zewe | MIT News |

Press Mentions

Teaching robots to socialize

Tech Crunch

Robots can deliver food on a college campus and hit a hole-in-one on the golf course, but even the most sophisticated robot can’t perform basic social interactions that are critical to everyday human life.

MIT researchers have now incorporated certain social interactions into a framework for robotics, enabling machines to understand what it means to help or hinder one another, and to learn to perform these social behaviors on their own. In a simulated environment, a robot watches its companion, guesses what task it wants to accomplish, and then helps or hinders this other robot based on its own goals.

The researchers also showed that their model creates realistic and predictable social interactions. When they showed videos of these simulated robots interacting with one another to humans, the human viewers mostly agreed with the model about what type of social behavior was occurring.

Enabling robots to exhibit social skills could lead to smoother and more positive human-robot interactions. For instance, a robot in an assisted living facility could use these capabilities to help create a more caring environment for elderly individuals. The new model may also enable scientists to measure social interactions quantitatively, which could help psychologists study autism or analyze the effects of antidepressants.

“Robots will live in our world soon enough, and they really need to learn how to communicate with us on human terms. They need to understand when it is time for them to help and when it is time for them to see what they can do to prevent something from happening. This is very early work and we are barely scratching the surface, but I feel like this is the first very serious attempt for understanding what it means for humans and machines to interact socially,” says Boris Katz, principal research scientist and head of the InfoLab Group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Center for Brains, Minds, and Machines (CBMM).

Joining Katz on the paper are co-lead author Ravi Tejwani, a research assistant at CSAIL; co-lead author Yen-Ling Kuo, a CSAIL PhD student; Tianmin Shu, a postdoc in the Department of Brain and Cognitive Sciences; and senior author Andrei Barbu, a research scientist at CSAIL and CBMM. The research will be presented at the Conference on Robot Learning in November.

A social simulation

To study social interactions, the researchers created a simulated environment where robots pursue physical and social goals as they move around a two-dimensional grid.

A physical goal relates to the environment. For example, a robot’s physical goal might be to navigate to a tree at a certain point on the grid. A social goal involves guessing what another robot is trying to do and then acting based on that estimation, like helping another robot water the tree.

The researchers use their model to specify what a robot’s physical goals are, what its social goals are, and how much emphasis it should place on one over the other. The robot is rewarded for actions it takes that get it closer to accomplishing its goals. If a robot is trying to help its companion, it adjusts its reward to match that of the other robot; if it is trying to hinder, it adjusts its reward to be the opposite. The planner, an algorithm that decides which actions the robot should take, uses this continually updating reward to guide the robot to carry out a blend of physical and social goals.

“We have opened a new mathematical framework for how you model social interaction between two agents. If you are a robot, and you want to go to location X, and I am another robot and I see that you are trying to go to location X, I can cooperate by helping you get to location X faster. That might mean moving X closer to you, finding another better X, or taking whatever action you had to take at X. Our formulation allows the plan to discover the ‘how’; we specify the ‘what’ in terms of what social interactions mean mathematically,” says Tejwani.

Blending a robot’s physical and social goals is important to create realistic interactions, since humans who help one another have limits to how far they will go. For instance, a rational person likely wouldn’t just hand a stranger their wallet, Barbu says.

The researchers used this mathematical framework to define three types of robots. A level 0 robot has only physical goals and cannot reason socially. A level 1 robot has physical and social goals but assumes all other robots only have physical goals. Level 1 robots can take actions based on the physical goals of other robots, like helping and hindering. A level 2 robot assumes other robots have social and physical goals; these robots can take more sophisticated actions like joining in to help together.

Evaluating the model

To see how their model compared to human perspectives about social interactions, they created 98 different scenarios with robots at levels 0, 1, and 2. Twelve humans watched 196 video clips of the robots interacting, and then were asked to estimate the physical and social goals of those robots.

In most instances, their model agreed with what the humans thought about the social interactions that were occurring in each frame.

“We have this long-term interest, both to build computational models for robots, but also to dig deeper into the human aspects of this. We want to find out what features from these videos humans are using to understand social interactions. Can we make an objective test for your ability to recognize social interactions? Maybe there is a way to teach people to recognize these social interactions and improve their abilities. We are a long way from this, but even just being able to measure social interactions effectively is a big step forward,” Barbu says.

Toward greater sophistication

The researchers are working on developing a system with 3D agents in an environment that allows many more types of interactions, such as the manipulation of household objects. They are also planning to modify their model to include environments where actions can fail.

The researchers also want to incorporate a neural network-based robot planner into the model, which learns from experience and performs faster. Finally, they hope to run an experiment to collect data about the features humans use to determine if two robots are engaging in a social interaction.

“Hopefully, we will have a benchmark that allows all researchers to work on these social interactions and inspire the kinds of science and engineering advances we’ve seen in other areas such as object and action recognition,” Barbu says.

“I think this is a lovely application of structured reasoning to a complex yet urgent challenge,” says Tomer Ullman, assistant professor in the Department of Psychology at Harvard University and head of the Computation, Cognition, and Development Lab, who was not involved with this research. “Even young infants seem to understand social interactions like helping and hindering, but we don’t yet have machines that can perform this reasoning at anything like human-level flexibility. I believe models like the ones proposed in this work, that have agents thinking about the rewards of others and socially planning how best to thwart or support them, are a good step in the right direction.”

This research was supported by the Center for Brains, Minds, and Machines; the National Science Foundation; the MIT CSAIL Systems that Learn Initiative; the MIT-IBM Watson AI Lab; the DARPA Artificial Social Intelligence for Successful Teams program; the U.S. Air Force Research Laboratory; the U.S. Air Force Artificial Intelligence Accelerator; and the Office of Naval Research.

Artificial intelligence sheds light on how the brain processes language

Anne Trafton |

In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type.

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion.

Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain.

Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.

“The better the model is at predicting the next word, the more closely it fits the human brain,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines (CBMM), and an author of the new study. “It’s amazing that the models fit so well, and it very indirectly suggests that maybe what the human language system is doing is predicting what’s going to happen next.”

Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of CBMM and MIT’s Artificial Intelligence Laboratory (CSAIL); and Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute, are the senior authors of the study, which appears this week in the Proceedings of the National Academy of Sciences.

Martin Schrimpf, an MIT graduate student who works in CBMM, is the first author of the paper.

Making predictions

The new, high-performing next-word prediction models belong to a class of models called deep neural networks. These networks contain computational “nodes” that form connections of varying strength, and layers that pass information between each other in prescribed ways.

Over the past decade, scientists have used deep neural networks to create models of vision that can recognize objects as well as the primate brain does. Research at MIT has also shown that the underlying function of visual object recognition models matches the organization of the primate visual cortex, even though those computer models were not specifically designed to mimic the brain.

In the new study, the MIT team used a similar approach to compare language-processing centers in the human brain with language-processing models. The researchers analyzed 43 different language models, including several that are optimized for next-word prediction. These include a model called GPT-3 (Generative Pre-trained Transformer 3), which, given a prompt, can generate text similar to what a human would produce. Other models were designed to perform different language tasks, such as filling in a blank in a sentence.

As each model was presented with a string of words, the researchers measured the activity of the nodes that make up the network. They then compared these patterns to activity in the human brain, measured in subjects performing three language tasks: listening to stories, reading sentences one at a time, and reading sentences in which one word is revealed at a time. These human datasets included functional magnetic resonance (fMRI) data and intracranial electrocorticographic measurements taken in people undergoing brain surgery for epilepsy.

They found that the best-performing next-word prediction models had activity patterns that very closely resembled those seen in the human brain. Activity in those same models was also highly correlated with measures of human behavioral measures such as how fast people were able to read the text.

“We found that the models that predict the neural responses well also tend to best predict human behavior responses, in the form of reading times. And then both of these are explained by the model performance on next-word prediction. This triangle really connects everything together,” Schrimpf says.

“A key takeaway from this work is that language processing is a highly constrained problem: The best solutions to it that AI engineers have created end up being similar, as this paper shows, to the solutions found by the evolutionary process that created the human brain. Since the AI network didn’t seek to mimic the brain directly — but does end up looking brain-like — this suggests that, in a sense, a kind of convergent evolution has occurred between AI and nature,” says Daniel Yamins, an assistant professor of psychology and computer science at Stanford University, who was not involved in the study.

Game changer

One of the key computational features of predictive models such as GPT-3 is an element known as a forward one-way predictive transformer. This kind of transformer is able to make predictions of what is going to come next, based on previous sequences. A significant feature of this transformer is that it can make predictions based on a very long prior context (hundreds of words), not just the last few words.

Scientists have not found any brain circuits or learning mechanisms that correspond to this type of processing, Tenenbaum says. However, the new findings are consistent with hypotheses that have been previously proposed that prediction is one of the key functions in language processing, he says.

“One of the challenges of language processing is the real-time aspect of it,” he says. “Language comes in, and you have to keep up with it and be able to make sense of it in real time.”

The researchers now plan to build variants of these language processing models to see how small changes in their architecture affect their performance and their ability to fit human neural data.

“For me, this result has been a game changer,” Fedorenko says. “It’s totally transforming my research program, because I would not have predicted that in my lifetime we would get to these computationally explicit models that capture enough about the brain so that we can actually leverage them in understanding how the brain works.”

The researchers also plan to try to combine these high-performing language models with some computer models Tenenbaum’s lab has previously developed that can perform other kinds of tasks such as constructing perceptual representations of the physical world.

“If we’re able to understand what these language models do and how they can connect to models which do things that are more like perceiving and thinking, then that can give us more integrative models of how things work in the brain,” Tenenbaum says. “This could take us toward better artificial intelligence models, as well as giving us better models of how more of the brain works and how general intelligence emerges, than we’ve had in the past.”

The research was funded by a Takeda Fellowship; the MIT Shoemaker Fellowship; the Semiconductor Research Corporation; the MIT Media Lab Consortia; the MIT Singleton Fellowship; the MIT Presidential Graduate Fellowship; the Friends of the McGovern Institute Fellowship; the MIT Center for Brains, Minds, and Machines, through the National Science Foundation; the National Institutes of Health; MIT’s Department of Brain and Cognitive Sciences; and the McGovern Institute.

Other authors of the paper are Idan Blank PhD ’16 and graduate students Greta Tuckute, Carina Kauf, and Eghbal Hosseini.