Author: Julie Pryor

An optimized solution for face recognition

by Jennifer Michalowski |

The human brain seems to care a lot about faces. It’s dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency—and neuroscientists at MIT’s McGovern Institute have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out.

The finding, reported March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.

“The human brain’s solution is to segregate the processing of faces from the processing of objects,” explains Katharina Dobs, who led the study as a postdoctoral researcher in McGovern investigator Nancy Kanwisher’s lab. The artificial network that she trained did the same. “And that’s the same solution that we hypothesize any system that’s trained to recognize faces and to categorize objects would find,” she adds.

“These two completely different systems have figured out what a—if not the—good solution is. And that feels very profound,” says Kanwisher.

Functionally specific brain regions

More than twenty years ago, Kanwisher’s team discovered a small spot in the brain’s temporal lobe that responds specifically to faces. This region, which they named the fusiform face area, is one of many brain regions Kanwisher and others have found that are dedicated to specific tasks, such as the detection of written words, the perception of vocal songs, and understanding language.

Kanwisher says that as she has explored how the human brain is organized, she has always been curious about the reasons for that organization. Does the brain really need special machinery for facial recognition and other functions? “‘Why questions’ are very difficult in science,” she says. But with a sophisticated type of machine learning called a deep neural network, her team could at least find out how a different system would handle a similar task.

Dobs, who is now a research group leader at Justus Liebig University Giessen in Germany, assembled hundreds of thousands of images with which to train a deep neural network in face and object recognition. The collection included the faces of more than 1,700 different people and hundreds of different kinds of objects, from chairs to cheeseburgers. All of these were presented to the network, with no clues about which was which. “We never told the system that some of those are faces, and some of those are objects. So it’s basically just one big task,” Dobs says. “It needs to recognize a face identity, as well as a bike or a pen.”

Visualization of the preferred stimulus for example face-ranked filters. While filters in early layers (e.g., Conv5) were maximally activated by simple features, filters responded to features that appear somewhat like face parts (e.g., nose and eyes) in mid-level layers (e.g., Conv9) and appear to represent faces in a more holistic manner in late convolutional layers. Image: Kanwisher lab

As the program learned to identify the objects and faces, it organized itself into an information-processing network with that included units specifically dedicated to face recognition. Like the brain, this specialization occurred during the later stages of image processing. In both the brain and the artificial network, early steps in facial recognition involve more general vision processing machinery, and final stages rely on face-dedicated components.

It’s not known how face-processing machinery arises in a developing brain, but based on their findings, Kanwisher and Dobs say networks don’t necessarily require an innate face-processing mechanism to acquire that specialization. “We didn’t build anything face-ish into our network,” Kanwisher says. “The networks managed to segregate themselves without being given a face-specific nudge.”

Kanwisher says it was thrilling seeing the deep neural network segregate itself into separate parts for face and object recognition. “That’s what we’ve been looking at in the brain for twenty-some years,” she says. “Why do we have a separate system for face recognition in the brain? This tells me it is because that is what an optimized solution looks like.”

Now, she is eager to use deep neural nets to ask similar questions about why other brain functions are organized the way they are. “We have a new way to ask why the brain is organized the way it is,” she says. “How much of the structure we see in human brains will arise spontaneously by training networks to do comparable tasks?”

School of Engineering welcomes new faculty

by School of Engineering |

The School of Engineering is welcoming 17 new faculty members to its departments, institutes, labs, and centers. With research and teaching activities ranging from the development of robotics and machine learning technologies to modeling the impact of elevated carbon dioxide levels on vegetation, they are poised to make significant contributions in new directions across the school and to a wide range of research efforts around the Institute.

“I am delighted to welcome our wonderful new faculty,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Their impact as talented educators, researchers, collaborators, and mentors will be felt across the School of Engineering and beyond as they strengthen our engineering community.”

Among the new faculty members are four from the Department of Electrical Engineering and Computer Science (EECS), which jointly reports into the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.

Iwnetim “Tim” Abate will join the Department of Materials Science and Engineering in July 2023. He is currently both a Miller and Presidential Postdoctoral Fellow at the University of California at Berkeley. He received his MS and PhD in materials science and engineering from Stanford University and BS in physics from Minnesota State University at Moorhead. He also has research experience in industry (IBM) and at national labs (Los Alamos and SLAC National Accelerator Laboratories). Utilizing computational and experimental approaches in tandem, his research program at MIT will focus on the intersection of material chemistry, electrochemistry, and condensed matter physics to develop solutions for climate change and smart agriculture, including next-generation battery and sensor devices. Abate is also a co-founder and president of a nonprofit organization, SciFro Inc., working on empowering the African youth and underrepresented minorities in the United States to solve local problems through scientific research and innovation. He will continue working on expanding the vision and impact of SciFro with the MIT community. Abate received the Dan Cubicciotti Award of the Electrochemical Society, the EDGE and DARE graduate fellowships, the United Technologies Research Center fellowship, the John Stevens Jr. Memorial Award and the Justice, Equity, Diversity and Inclusion Graduation Award from Stanford University. He will hold the Toyota Career Development Professorship at MIT.

Kaitlyn Becker will join the Department of Mechanical Engineering as an assistant professor in August 2022. Becker received her PhD in materials science and mechanical engineering from Harvard University in 2021 and previously worked in industry as a manufacturing engineer at Cameron Health and a senior engineer for Nano Terra, Inc. She is a postdoc at the Harvard University School of Engineering and Applied Sciences and is also currently a senior glassblowing instructor in the Department of Materials Science and Engineering at MIT. Becker works on adaptive soft robots for grasping and manipulation of delicate structures from the desktop to the deep sea. Her research focuses on novel soft robotic platforms, adding functionality through innovations at the intersection of design and fabrication. She has developed novel fabrication methodologies and mechanical programming methods for large integrated arrays of soft actuators capable of collective manipulation and locomotion, and demonstrated integration of microfluidic circuits to control arrays of multichannel, two-degrees-of-freedom soft actuators. Becker received the National Science Foundation Graduate Research Fellowship in 2015, the Microsoft Graduate Women’s Scholarship in 2015, the Winston Chen Graduate Fellowship in 2015, and the Courtlandt S. Gross Memorial Scholarship in 2014.

Brandon J. DeKosky joined the Department of Chemical Engineering as an assistant professor in a newly introduced joint faculty position between the department and the Ragon Institute of MGH, MIT, and Harvard in September 2021. He received his BS in chemical engineering from University of Kansas and his PhD in chemical engineering from the University of Texas at Austin. He then did postdoctoral research at the Vaccine Research Center of the National Institute of Infectious Diseases. In 2017, Brandon launched his independent academic career as an assistant professor at the University of Kansas in a joint position with the Department of Chemical Engineering and the Department of Pharmaceutical Chemistry. He was also a member of the bioengineering graduate program. His research program focuses on developing and applying a suite of new high-throughput experimental and computational platforms for molecular analysis of adaptive immune responses, to accelerate precision drug discovery. He has received several notable recognitions, which include receipt of the NIH K99 Path to Independence and NIH DP5 Early Independence awards, the Cellular and Molecular Bioengineering Rising Star Award from the Biomedical Engineering Society, and the Career Development Award from the Congressionally Directed Medical Research Program’s Peer Reviewed Cancer Research Program.

Mohsen Ghaffari will join the Department of Electrical Engineering and Computer Science in April 2022. He received his BS from the Sharif University of Technology, and his MS and PhD in EECS from MIT. His research focuses on distributed and parallel algorithms for large graphs. Ghaffari received the ACM Doctoral Dissertation Honorable Mention Award, the ACM-EATCS Principles of Distributed Computing Doctoral Dissertation Award, and the George M. Sprowls Award for Best Computer Science PhD thesis at MIT. Before coming to MIT, he was on the faculty at ETH Zurich, where he received a prestigious European Research Council Starting Grant.

Aristide Gumyusenge joined the Department of Materials Science and Engineering in January. He is currently a postdoc at Stanford University working with Professor Zhenan Bao and Professor Alberto Salleo. He received a BS in chemistry from Wofford College in 2015 and a PhD in chemistry from Purdue University in 2019. His research background and interests are in semiconducting polymers, their processing and characterization, and their unique role in the future of electronics. Particularly, he has tackled longstanding challenges in operation stability of semiconducting polymers under extreme heat and has pioneered high-temperature plastic electronics. He has been selected as a PMSE Future Faculty Scholar (2021), the GLAM Postdoctoral Fellow (2020-22), and the MRS Arthur Nowick and Graduate Student Gold Awardee (2019), among other recognitions. At MIT, he will lead the Laboratory of Organic Materials for Smart Electronics (OMSE Lab). Through polymer design, novel processing strategies, and large-area manufacturing of electronic devices, he is interested in relating molecular design to device performance, especially transistor devices able to mimic and interface with biological systems. He will hold the Merton C. Flemings Career Development Professorship.

Mina Konakovic Lukovic will join the Department of Electrical Engineering and Computer Science as an assistant professor in July 2022. She received her BS and MS from the University of Belgrade, Faculty of Mathematics. She earned her PhD in 2019 in the School of Computer and Communication Sciences at the Swiss Federal Institute of Technology Lausanne, advised by Professor Mark Pauly. Currently a Schmidt Science Postdoctoral Fellow in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), she has been mentored by Professor Wojciech Matusik. Her research focuses on computer graphics, computational fabrication, 3D geometry processing, and machine learning, including architectural geometry and the design of programmable materials. She received the ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention, the Eurographics PhD Award, and was recently awarded the 2021 SIAM Activity Group on Geometric Design Early Career Prize.

Darcy McRose will join the Department of Civil and Environmental Engineering as an assistant professor in August 2022. She completed a BS in Earth systems at Stanford and a PhD in geosciences at Princeton University. Darcy is currently conducting postdoctoral work at Caltech, where she is mentored by Professor Dianne Newman in the divisions of Biology and Biological Engineering and Geological and Planetary Sciences. Her research program focuses on microbe-environment interactions and their effects on biogeochemical cycles, and incorporates techniques ranging from microbial physiology and genetics to geochemistry. A particular emphasis for this work is the production and use of secondary metabolites and small molecules in soils and sediments. McRose received the Caltech BBE Division postdoctoral fellowship in 2019 and is currently a Simons Foundation Marine Microbial Ecology postdoctoral fellow as well as a L’Oréal USA for Women in Science fellow.

Qin (Maggie) Qi joined the Department of Chemical Engineering as an assistant professor in January 2022. She received two BS degrees in chemical engineering and in operations research from Cornell University, before moving on to Stanford for her PhD. She then took on a postdoc position at Harvard University School of Engineering and Applied Sciences and the Wyss Institute. Maggie’s proposed research includes combining extensive theoretical and computational work on predictive models that guide experimental design. She seeks to investigate particle-cell biomechanics and function for better targeted cell-based therapies. She also plans to design microphysiological systems that elucidate hydrodynamics in complex organs, including delivery of drugs to the eye, and to examine ionic liquids as complex fluids for biomaterial design. She aims to push the boundaries of fluid mechanics, transport phenomena, and soft matter for human health and to innovate precision health care solutions. Maggie received the T.S. Lo Graduate Fellowship and the Stanford Graduate Fellowship in Science and Engineering. Among her accomplishments, Maggie was a participant in the inaugural class of the MIT Rising Stars in ChemE Program in 2018.

Manish Raghavan will join the MIT Sloan School of Management and the Department of Electrical Engineering and Computer Science as an assistant professor in September 2022. He shares a joint appointment with the MIT Schwarzman College of Computing. He received a bachelor’s degree in electrical engineering and computer science from the University of California at Berkeley, and PhD from the Computer Science department at Cornell University. Prior to joining MIT, he was a postdoc at the Harvard Center for Research on Computation and Society. His research interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. He is also a member of Cornell’s Artificial Intelligence, Policy, and Practice initiative and Mechanism Design for Social Good.

Ritu Raman joined the Department of Mechanical Engineering as an assistant professor and Brit and Alex d’Arbeloff Career Development Chair in August 2021. Raman received her PhD in mechanical engineering from the University of Illinois at Urbana-Champaign as an NSF Graduate Research Fellow in 2016 and completed a postdoctoral fellowship with Professor Robert Langer at MIT, funded by a NASEM Ford Foundation Fellowship and a L’Oréal USA For Women in Science Fellowship. Raman’s lab designs adaptive living materials powered by assemblies of living cells for applications ranging from medicine to machines. Currently, she is focused on using biological materials and engineering tools to build living neuromuscular tissues. Her goal is to help restore mobility to those who have lost it after disease or trauma and to deploy biological actuators as functional components in machines. Raman published the book Biofrabrication with MIT Press in September 2021. She was in the MIT Technology Review “35 Innovators Under 35” 2019 class, the Forbes “30 Under 30” 2018 class, and has received numerous awards including being named a National Academy of Sciences Kavli Frontiers of Science Fellow in 2020 and receiving the Science and Sartorius Prize for Regenerative Medicine and Cell Therapy in 2019. Ritu has championed many initiatives to empower women in science, including being named an AAAS IF/THEN ambassador and founding the Women in Innovation and Stem Database at MIT (WISDM).

Nidhi Seethapathi joined the Department of Brain and Cognitive Sciences and the Department of Electrical Engineering and Computer Science in January 2022. She shares a joint appointment with the MIT Schwarzman College of Computing. She received a bachelor’s degree in mechanical engineering from Veermata Jijabai Technological Institute and a PhD from the Movement Lab at Ohio State University. Her research interests include building computational predictive models of human movement with applications to autonomous and robot-aided neuromotor rehabilitation. In her work, she uses a combination of tools and approaches from dynamics, control theory, and machine learning. During her PhD, she was a Schlumberger Foundation Faculty for the Future Fellow. She then worked as a postdoc in the Kording Lab at University of Pennsylvania, developing data-driven tools for autonomous neuromotor rehabilitation, in collaboration with the Rehabilitation Robotics Lab.

Vincent Sitzmann will join the Department of Electrical Engineering and Computer Science as an assistant professor in July 2022. He earned his BS from the Technical University of Munich in 2015, his MS from Stanford in 2017, and his PhD from Stanford in 2020. At MIT, he will be the principal investigator of the Scene Representation Group, where he will lead research at the intersection of machine learning, graphics, neural rendering, and computer vision to build algorithms that learn to reconstruct, understand, and interact with 3D environments from incomplete observations the way humans can. Currently, Vincent is a postdoc at the MIT Computer Science and Artificial Intelligence Laboratory with Josh Tenenbaum, Bill Freeman, and Fredo Durand. Along with multiple scholarships and fellowships, he has been recognized with the NeurIPS Honorable Mention: Outstanding New Directions in 2019.

Tess Smidt joined the Department of Electrical Engineering and Computer Science as an assistant professor in September 2021. She earned her SB in physics from MIT in 2012 and her PhD in physics from the University of California at Berkeley in 2018. She is the principal investigator of the Atomic Architects group at the Research Laboratory of Electronics, where she works at the intersection of physics, geometry, and machine learning to design algorithms that aid in the understanding and design of physical systems. Her research focuses on machine learning that incorporates physical and geometric constraints, with applications to materials design. Prior to joining the MIT EECS faculty, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory and a software engineering intern on the Google Accelerated Sciences team, where she developed Euclidean symmetry equivariant neural networks which naturally handle 3D geometry and geometric tensor data.

Loza Tadesse will join the Department of Mechanical Engineering as an assistant professor in July 2023. She received her PhD in bioengineering from Stanford University in 2021 and previously was a medical student at St. Paul Hospital Millennium Medical College in Ethiopia. She is currently a postdoc at the University of California at Berkeley. Tadesse’s past research combines Raman spectroscopy and machine learning to develop a rapid, all-optical, and label-free bacterial diagnostic and antibiotic susceptibility testing system that aims to circumvent the time-consuming culturing step in “gold standard” methods. She aims to establish a research program that develops next-generation point-of-care diagnostic devices using spectroscopy, optical, and machine learning tools for application in resource limited clinical settings such as developing nations, military sites, and space exploration. Tadesse has been listed as a 2022 Forbes “30 Under 30” in health care, received many awards including the Biomedical Engineering Society (BMES) Career Development Award, the Stanford DARE Fellowship and the Gates Foundation “Call to Action” $200,000 grant for SciFro Inc., an educational nonprofit in Ethiopia, which she co-founded.

César Terrer joined the Department of Civil and Environmental Engineering as an assistant professor in July 2021. He obtained his PhD in ecosystem ecology and climate change from Imperial College London, where he started working at the interface between experiments and models to better understand the effects of elevated carbon dioxide on vegetation. His research has advanced the understanding on the effects of carbon dioxide in terrestrial ecosystems, the role of soil nutrients in a climate change context, and plant-soil interactions. Synthesizing observational data from carbon dioxide experiments and satellites through meta-analysis and machine learning, César has found that microbial interactions between plants and soils play a major role in the carbon cycle at a global scale, affecting the speed of global warming.

Haruko Wainwright joined the Department of Nuclear Science and Engineering as an assistant professor in January 2021. She received her BEng in engineering physics from Kyoto University, Japan in 2003, her MS in nuclear engineering in 2006, her MA in statistics in 2010, and her PhD in nuclear engineering in 2010 from University of California at Berkeley. Before joining MIT, she was a staff scientist in the Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory and an adjunct professor in nuclear engineering at UC Berkeley. Her research focuses on environmental modeling and monitoring technologies, with a particular emphasis on nuclear waste and nuclear-related contamination. She has been developing Bayesian methods for multi-type multiscale data integration and model-data integration. She leads and co-leads multiple interdisciplinary projects, including the U.S. Department of Energy’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project, and the Artificial Intelligence for Earth System Predictability (AI4ESP) initiative.

Martin Wainwright will join the Department of Electrical Engineering and Computer Science in July 2022. He received a bachelor’s degree in mathematics from University of Waterloo, Canada, and PhD in EECS from MIT. Prior to joining MIT, he was the Chancellor’s Professor at the University of California at Berkeley, with a joint appointment between the Department of Statistics and the Department of EECS. His research interests include high-dimensional statistics, statistical machine learning, information theory, and optimization theory. Among other awards, he has received the COPSS Presidents’ Award (2014) from the Joint Statistical Societies, the David Blackwell Lectureship (2017), and Medallion Lectureship (2013) from the Institute of Mathematical Statistics, and Best Paper awards from the IEEE Signal Processing Society and IEEE Information Theory Society. He was a Section Lecturer at the International Congress of Mathematicians in 2014.

 

Augmented: The journey of Hugh Herr

by Julie Pryor |

Augmented is a Nova PBS documentary that premiered in February 2022, featuring Hugh Herr, the co-director of the K. Lisa Yang Center for Bionics at MIT.

Follow the dramatic personal journey of Hugh Herr, a biophysicist working to create brain-controlled robotic limbs. At age 17, Herr’s legs were amputated after a climbing accident. Frustrated by the crude prosthetic limbs he was given, Herr set out to remedy their design, leading him to a career as an inventor of innovative prosthetic devices. Now, Herr is teaming up with an injured climber and a surgeon at a leading Boston hospital to test a new approach to surgical amputation that allows prosthetic limbs to move and feel like the real thing. Herr’s journey is a powerful tale of innovation and the inspiring story of a personal tragedy transformed into a life-long quest to help others.

Read more at PBS.org.

David Ginty named winner of 2022 Scolnick Prize

by Jill Crittenden |

Harvard neurobiologist David Ginty, winner of the 2022 Scolnick Prize.

The McGovern Institute for Brain Research announced today that Harvard neurobiologist David D. Ginty has been selected for the 2022 Edward M. Scolnick Prize in Neuroscience. Ginty, who is the Edward R. and Anne G. Lefler Professor of Neurobiology at Harvard Medical School, is being recognized for his fundamental discoveries into the neural mechanisms underlying the sense of touch. The Scolnick Prize is awarded annually by the McGovern Institute for outstanding advances in neuroscience.

“David Ginty has made seminal contributions in basic research that also have important translational implications,” says Robert Desimone, McGovern Institute Director and chair of the selection committee. “His rigorous research has led us to understand how the peripheral nervous system encodes the overall perception of touch, and how molecular mechanisms underlying this can fail in disease states.”

Ginty obtained his PhD in 1989 with Edward Seidel where he studied cell proliferation factors. He went on to a postdoctoral fellowship researching nerve growth factor with John Wagner at the Dana-Farber Cancer Institute and, upon Wagner’s departure to Cornell, transferred to Michael Greenberg’s lab at Harvard Medical School. There, he dissected intracellular signaling pathways for neuronal growth factors and neurotransmitters and developed key antibody reagents to detect activated forms of transcription factors. These antibody tools are now used by labs around the world in the research of neuronal plasticity and brain disorders, including Alzheimer’s disease and schizophrenia.

In 1995, Ginty started his own laboratory at Johns Hopkins University with a focus on the development and functional organization of the peripheral nervous system. Ginty’s group created and applied the latest genetic engineering techniques in mice to uncover how the peripheral nervous system develops and is organized at the molecular, cellular and circuit levels to perceive touch. Most notably, using gene targeting combined with electrophysiological, behavioral and anatomical analyses, the Ginty lab untangled properties and functions of the different types of touch neurons, termed low- and high-threshold mechanoreceptors, that convey distinct aspects of stimulus information from the skin to the central nervous system. Ginty and colleagues also discovered organizational principles of spinal cord and brainstem circuits dedicated to touch information processing, and that integration of signals from the different mechanoreceptor types begins within spinal cord networks before signal transmission to the brain.

In 2013, Ginty joined the faculty of Harvard Medical School where his team applied their genetic tools and techniques to probe the neural basis of touch sensitivity disorders. They discovered properties and functions of peripheral sensory neurons, spinal cord circuits, and ascending pathways that transmit noxious, painful stimuli from the periphery to the brain. They also asked whether abnormalities in peripheral nervous system function lead to touch over-reactivity in cases of autism or in neuropathic pain caused by nerve injury, chemotherapy, or diabetes, where even a soft touch can be aversive or painful. His team found that sensory abnormalities observed in several mouse models of autism spectrum disorder could be traced to peripheral mechanosensory neurons. They also found that reducing the activity of peripheral sensory neurons prevented tactile over-reactivity in these models and even, in some cases, lessened anxiety and abnormal social behaviors. These findings provided a plausible explanation for how sensory dysfunction may contribute to physiological and cognitive impairments in autism. Importantly, this laid the groundwork for a new approach and initiative to identify new potential therapies for disorders of touch and pain.

Ginty was named a Howard Hughes Medical Institute Investigator in 2000 and was elected to the American Academy of Arts and Sciences in 2015 and the National Academy of Sciences in 2017. He shared Columbia University’s Alden W. Spencer Prize with Ardem Patapoutian in 2017 and was awarded the Society for Neuroscience Julius Axelrod Prize in 2021. Ginty is also known for exceptional mentorship. He directed the neuroscience graduate program at Johns Hopkins from 2006 to 2013 and now serves as the associate director of Harvard’s neurobiology graduate program.

The McGovern Institute will award the Scolnick Prize to Ginty on Wednesday, June 1, 2022. At 4:00 pm he will deliver a lecture entitled “The sensory neurons of touch: beauty is skin deep,” to be followed by a reception at the McGovern Institute, 43 Vassar Street (building 46, room 3002) in Cambridge. The event is free and open to the public; registration is required.

Seven new faculty join the MIT School of Science

by School of Science |

This winter, seven new faculty members join the MIT School of Science in the departments of Biology and Brain and Cognitive Sciences.

Siniša Hrvatin studies how animals initiate, regulate, and survive states of stasis, such as torpor and hibernation. To survive extreme environments, many animals have evolved the ability to decrease metabolic rate and body temperature and enter dormant states. His long-term goal is to harness the potential of these biological adaptations to advance medicine. Previously, he identified the neurons that regulate mouse torpor and established a platform for the development of cell-type-specific viral drivers.
Hrvatin earned his bachelor’s degree in biochemical sciences in 2007 and his PhD in stem cell and regenerative medicine in 2013, both from Harvard University. He was then a postdoc in bioengineering at MIT and a postdoc in neurobiology at Harvard Medical School. Hrvatin returns to MIT as an assistant professor of biology and a member of the Whitehead Institute for Biomedical Research.

Sara Prescott investigates how sensory inputs from within the body control mammalian physiology and behavior. Specifically, she uses mammalian airways as a model system to explore how the cells that line the surface of the body communicate with parts of the nervous system. For example, what mechanisms elicit a reflexive cough? Prescott’s research considers the critical questions of how airway insults are detected, encoded, and adapted to mammalian airways with the ultimate goal of providing new ways to treat autonomic dysfunction.

Prescott earned her bachelor’s degree in molecular biology from Princeton University in 2008 followed by her PhD in developmental biology from Stanford University in 2016. Prior to joining MIT, she was a postdoc at Harvard Medical School and Howard Hughes Medical Institute. The Department of Biology welcomes Prescott as an assistant professor.
Alison Ringel is a T-cell immunologist with a background in biochemistry, biophysics, and structural biology. She investigates how environmental factors such as aging, metabolism, and diet impact tumor progress and the immune responses that cause tumor control. By mapping the environment around a tumor on a cellular level, she seeks to gain a molecular understanding of cancer risk factors.

Ringel received a bachelor’s degree in molecular biology, biochemistry, and physics from Wesleyan University, then a PhD in molecular biophysics from John Hopkins University School of Medicine. Previously, Ringel was a postdoc in the Department of Cell Biology at Harvard Medical School. She joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute of MGH, MIT and Harvard.

Francisco J. Sánchez-Rivera PhD ’16 investigates genetic variation with a focus on cancer. He integrates genome engineering technologies, genetically-engineered mouse models (GEMMs), and single cell lineage tracing and omics approaches in order to understand the mechanics of cancer development and evolution. With state-of-the-art technologies — including a CRISPR-based genome editing system he developed as a graduate student at MIT — he hopes to make discoveries in cancer genetics that will shed light on disease progression and pave the way for better therapeutic treatments.

Sánchez-Rivera received his bachelor’s degree in microbiology from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT. He then pursued postdoctoral studies at Memorial Sloan Kettering Cancer Center supported by a HHMI Hanna Gray Fellowship. Sánchez-Rivera returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute for Integrative Cancer Research at MIT.

Nidhi Seethapathi builds predictive models to help understand human movement with a combination of theory, computational modeling, and experiments. Her research focuses on understanding the objectives that govern movement decisions, the strategies used to execute movement, and how new movements are learned. By studying movement in real-world contexts using creative approaches, Seethapathi aims to make discoveries and develop tools that could improve neuromotor rehabilitation.

Seethapathi earned her bachelor’s degree in mechanical engineering from the Veermata Jijabai Technological Institute followed by her PhD in mechanical engineering from Ohio State University. In 2018, she continued to the University of Pennsylvania where she was a postdoc. She joins MIT as an assistant professor in the Department of Brain and Cognitive Sciences with a shared appointment in the Department of Electrical Engineering and Computer Science at the MIT Schwarzman College of Computing.

Hernandez Moura Silva researches how the immune system supports tissue physiology. Silva focuses on macrophages, a type of immune cell involved in tissue homeostasis. He plans to establish new strategies to explore the effects and mechanisms of such immune-related pathways, his research ultimately leading to the development of therapeutic approaches to treat human diseases.

Silva earned a bachelor’s degree in biological sciences and a master’s degree in molecular biology from the University of Brasilia. He continued to complete a PhD in immunology at the University of São Paulo School of Medicine: Heart Institute. Most recently, he acted as the Bernard Levine Postdoctoral Fellow in immunology and immuno-metabolism at the New York University School of Medicine: Skirball Institute of Biomolecular Medicine. Silva joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute.

Yadira Soto-Feliciano PhD ’16 studies chromatin — the complex of DNA and proteins that make up chromosomes. She combines cancer biology and epigenetics to understand how certain proteins affect gene expression and, in turn, how they impact the development of cancer and other diseases. In decoding the chemical language of chromatin, Soto-Feliciano pursues a basic understanding of gene regulation that could improve the clinical management of diseases associated with their dysfunction.

Soto-Feliciano received her bachelor’s degree in chemistry from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT, where she was also a research fellow with the Koch Institute. Most recently, she was the Damon Runyon-Sohn Pediatric Cancer Postdoctoral Fellow at The Rockefeller University. Soto-Feliciano returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute.

A new approach to curbing cocaine use

by Jennifer Michalowski |

Cocaine, opioids, and other drugs of abuse disrupt the brain’s reward system, often shifting users’ priorities to obtaining more drug above all else. For people battling addiction, this persistent craving is notoriously difficult to overcome—but new research from scientists at MIT’s McGovern Institute and collaborators points toward a therapeutic strategy that could help.

Researchers in MIT Institute Professor Ann Graybiel’s lab and collaborators at the University of Copenhagen and Vanderbilt University report in a January 25, 2022 online publication in the journal Addiction Biology that activating a signaling molecule in the brain known as muscarinic receptor 4 (M4) causes rodents to reduce cocaine self-administration and simultaneously choose a food treat over cocaine.

M4 receptors are found on the surface of neurons in the brain, where they alter signaling in response to the neurotransmitter acetylcholine. They are plentiful in the striatum, a brain region that Graybiel’s lab has shown is deeply involved in habit formation. They are of interest to addiction researchers because, along with a related receptor called M1, which is also abundant in the striatum, they often seem to act in opposition to the neurotransmitter dopamine.

Drugs of abuse stimulate the brain’s habit circuits by allowing dopamine to build up in the brain. With chronic use, that circuitry can become less sensitive to dopamine, so experiences that were once rewarding become less pleasurable and users are driven to seek higher doses of their drug. Attempts to directly block the dopamine system have not been found to be an effective way of treating addiction and can have unpleasant or dangerous side-effects, so researchers are seeking an alternative strategy to restore balance within the brain’s reward circuitry. “Another way to tweak that system is to activate these muscarinic receptors,” explains Jill Crittenden, a research scientist in the Graybiel lab.

New pathways to treatment

At the University of Copenhagen, neuroscientist Morgane Thomsen has found that activating the M1 receptor causes rodents to choose a food treat over cocaine. In the new work, she showed that a drug that selectively activates the M4 receptor has a similar effect.

When rats that have been trained to self-administer cocaine are given an M4-activating compound, they immediately reduce their drug use, actively choosing food instead. Thomsen found that this effect grew stronger over a seven-day course of treatment, with cocaine use declining day by day. When the M4-activating treatment was stopped, rats quickly resumed their prior cocaine-seeking behavior.

While Thomsen’s experiments have now shown that animals’ cocaine use can be reduced by activating either M1 or M4, it’s clear that the two muscarinic receptors don’t modulate cocaine use in the same way. M1 activation works on a different time scale, taking some time to kick in, but leaving some lasting effects even after the treatment has been discontinued.

Experiments with genetically modified mice developed in Graybiel’s lab confirm that the two receptors influence drug-seeking behavior via different molecular pathways. Previously, the team discovered that activating M1 has no effect on cocaine-seeking in mice that lack a signaling molecule called CalDAG-GEFI. M4 activation, however, reduces cocaine consumption regardless of whether CalDAG-GEFI is present. “The CalDAG-GEFI is completely essential for the M1 effect to happen, but doesn’t appear to play any role in the M4 effect,” Thomsen says. “So that really separates the pathways. In both the behavior and the neurobiology, it’s two different ways that we can modulate the cocaine effects.” The findings suggest that activating M4 could help people with substance abuse disorders overcome their addiction, and that such a strategy might be even more effective if combined with activation of the M1 receptor.

Graybiel’s lab first became interested in CalDAG-GEFI in the late 1990s, when they discovered that it was unusually abundant in the main compartment of the brain’s striatum. Their research revealed the protein to be important for controlling movement and even uncovered an essential role in blood clotting—but CalDAG-GEFI’s impacts on behavior remained elusive for a long time. Graybiel says it’s gratifying that this long-standing interest has now shed light on a potential therapeutic strategy for substance abuse disorder. Her lab will continue investigating the molecular pathways that underlie addiction as part of the McGovern Institute’s new addiction initiative.

Assessing connections in the brain’s reading network

by Jennifer Michalowski |

When we read, information zips between language processing centers in different parts of the brain, traveling along neural highways in the white matter. This coordinated activity allows us to decipher words and comprehend their meaning. Many neuroscientists suspect that variations in white matter may underlie differences in reading ability, and hope that by determining which white matter tracts are involved, they will be able to guide the development of more effective interventions for children who struggle with reading skills.

In a January 14, 2022, online publication in the journal NeuroImage, scientists at MIT’s McGovern Institute report on the largest brain imaging study to date to evaluate the relationship between white matter structure and reading ability. Their findings suggest that if white matter deficiencies are a significant cause of reading disability, new strategies will be needed to pin them down.

White matter is composed of bundles of insulated nerve fibers. It can be thought of as the internet of the brain, says senior author John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology at MIT. “It’s the connectivity: the way that the brain communicates at some distance to orchestrate higher-level thoughts, and abilities like reading,” explains Gabrieli, who is also a professor of brain and cognitive sciences and an investigator at the McGovern Institute.

The left inferior cerebellar peduncle, a white matter tract that connects the cerebellum to the brainstem and spinal cord. Image: Steven Meisler

Long-distance connections

To visualize white matter and study its structure, neuroscientists use an imaging technique called diffusion-weighted imaging (DWI). Images are collected in an MRI scanner by tracking the movements of water molecules in the brain. A key measure used to interpret these images is fractional anisotropy (FA), which varies with many physical features of nerve fibers, such as their density, diameter, and degree of insulation. Although FA does not measure any of these properties directly, it is considered an indicator of structural integrity within white matter tracts.

Several studies have found the FA of one or more white matter tracts to be lower in children with low reading scores or dyslexia than in children with stronger reading abilities. But those studies are small—usually involving only a few dozen children—and their findings are inconsistent. So it has been difficult to attribute reading problems to poor connections between specific parts of the brain.

Hoping to glean more conclusive results, Gabrieli and Steven Meisler, a graduate student in the Harvard Program in Speech and Hearing Bioscience and Technology who is completing his doctoral work in the Gabrieli lab, turned to a large collection of high-quality brain images available through the Child Mind Institute’s Healthy Brain Network. Using DWI images collected from 686 children and state-of-the-art methods of analysis, they assessed the FA of 20 white matter tracts that are thought to be important for reading.

The children represented in the dataset had diverse reading abilities, but surprisingly, when they compared children with and without reading disability, Meisler and Gabrieli found no significant differences in the FA of any of the 20 tracts. Nor did they find any correlation between white matter FA and children’s overall reading scores.

More detailed analysis did link reading ability to the FA of two particular white matter tracts. The researchers only detected the correlation when they narrowed their analysis to children older than eight, who are usually reading to learn, rather than learning to read. Within this group, they found two white matter tracts whose FA was lower in children who struggled with a specific reading skill: reading “pseudowords.” The ability to read nonsense words is used to assess knowledge of the relationship between letters and sounds, since real words can be recognized instead through experience and memory.

The right superior longitudinal fasciculus, a white matter tract that connects frontal brain regions to parietal areas. The research team found that fractional anisotropy (FA) of the right superior longitudinal fasciculus and the left inferior cerebellar peduncles (shown above) correlated positively with pseudoword reading ability among children ages 9 and older. Image: Steven Meisler

The first of these tracts connects language processing centers in the frontal and parietal brain regions. The other contains fibers that connect that the brainstem with the cerebellum, and may help control the eye movements needed to see and track words. The FA differences that Meisler and Gabrieli linked to reading scores were small, and it’s not yet clear what they mean. Since less cohesive structure in these two tracts was linked to lower pseudoword-reading scores only in older children, it may be a consequence of living with a reading disability rather than a cause, Meisler says.

The findings don’t rule out a role for white matter structure in reading disability, but they do suggest that researchers will need a different approach to find relevant features. “Our results suggest that FA does not relate to reading abilities as much as previously thought,” Meisler says. In future studies, he says, researchers will likely need to take advantage of more advanced methods of image analysis to assess features that more directly reflect white matter’s ability to serve as a conduit of information.

National Academy of Sciences honors cognitive neuroscientist Nancy Kanwisher

by Julie Pryor |

MIT neuroscientist and McGovern Investigator Nancy Kanwisher. Photo: Jussi Puikkonen/KNAW

The National Academy of Sciences (NAS) has announced today that Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience in MIT’s Department of Brain and Cognitive Sciences, has received the 2022 NAS Award in the Neurosciences for her “pioneering research into the functional organization of the human brain.” The $25,000 prize, established by the Fidia Research Foundation, is presented every three years to recognize “extraordinary contributions to the neuroscience fields.”

“I am deeply honored to receive this award from the NAS,” says Kanwisher, who is also an investigator in MIT’s McGovern Institute and a member of the Center for Brains, Minds and Machines. “It has been a profound privilege, and a total blast, to watch the human brain in action as these data began to reveal an initial picture of the organization of the human mind. But the biggest joy has been the opportunity to work with the incredible group of talented young scientists who actually did the work that this award recognizes.”

A window into the mind

Kanwisher is best known for her landmark insights into how humans recognize and process faces. Psychology had long-suggested that recognizing a face might be distinct from general object recognition. But Kanwisher galvanized the field in 1997 with her seminal discovery that the human brain contains a small region specialized to respond only to faces. The region, which Kanwisher termed the fusiform face area (FFA), became activated when subjects viewed images of faces in an MRI scanner, but not when they looked at scrambled faces or control stimuli.

Since her 1997 discovery (now the most highly cited manuscript in its area), Kanwisher and her students have applied similar methods to find brain specializations for the recognition of scenes, the mental states of others, language, and music. Taken together, her research provides a compelling glimpse into the architecture of the brain, and, ultimately, what makes us human.

“Nancy’s work over the past two decades has argued that many aspects of human cognition are supported by specialized neural circuitry, a conclusion that stands in contrast to our subjective sense of a singular mental experience,” says McGovern Institute Director Robert Desimone. “She has made profound contributions to the psychological and cognitive sciences and I am delighted that the National Academy of Sciences has recognized her outstanding achievements.”

One-in-a-million mentor

Beyond the lab, Kanwisher has a reputation as a tireless communicator and mentor who is actively engaged in the policy implications of brain research. The statistics speak for themselves: her 2014 TED talk, “A Neural portrait of the human mind” has been viewed over a million times online and her introductory MIT OCW course on the human brain has generated more than nine million views on YouTube.

Nancy Kanwisher works with researchers from her lab in MIT’s Martinos Imaging Center. Photo: Kris Brewer

Kanwisher also has an exceptional track record in training women scientists who have gone on to successful independent research careers, in many cases becoming prominent figures in their own right.

“Nancy is the one-in-a-million mentor, who is always skeptical of your ideas and your arguments, but immensely confident of your worth,” says Rebecca Saxe, John W. Jarve (1978) Professor of Brain and Cognitive Sciences, investigator at the McGovern Institute, and associate dean of MIT’s School of Science. Saxe was a graduate student in Kanwisher’s lab where she earned her PhD in cognitive neuroscience in 2003. “She has such authentic curiosity,” Saxe adds. “It’s infectious and sustaining. Working with Nancy was a constant reminder of why I wanted to be a scientist.”

The NAS will present Kanwisher with the award during its annual meeting on May 1, 2022 in Washington, DC. The event will be webcast live. Kanwisher plans to direct her prize funds to the non-profit organization Malengo, established by a former student and which provides quality undergraduate education to individuals who would otherwise not be able to afford it.

The craving state

by Jennifer Michalowski |

This story originally appeared in the Winter 2022 issue of BrainScan.

***

For people struggling with substance use disorders — and there are about 35 million of them worldwide — treatment options are limited. Even among those who seek help, relapse is common. In the United States, an epidemic of opioid addiction has been declared a public health emergency.

A 2019 survey found that 1.6 million people nationwide had an opioid use disorder, and the crisis has surged since the start of the COVID-19 pandemic. The Centers for Disease Control and Prevention estimates that more than 100,000 people died of drug overdose between April 2020 and April 2021 — nearly 30 percent more overdose deaths than occurred during the same period the previous year.

In the United States, an epidemic of opioid addiction has been declared a public health emergency.

A deeper understanding of what addiction does to the brain and body is urgently needed to pave the way to interventions that reliably release affected individuals from its grip. At the McGovern Institute, researchers are turning their attention to addiction’s driving force: the deep, recurring craving that makes people prioritize drug use over all other wants and needs.

McGovern Institute co-founder, Lore Harp McGovern.

“When you are in that state, then it seems nothing else matters,” says McGovern Investigator Fan Wang. “At that moment, you can discard everything: your relationship, your house, your job, everything. You only want the drug.”

With a new addiction initiative catalyzed by generous gifts from Institute co-founder Lore Harp McGovern and others, McGovern scientists with diverse expertise have come together to begin clarifying the neurobiology that underlies the craving state. They plan to dissect the neural transformations associated with craving at every level — from the drug-induced chemical changes that alter neuronal connections and activity to how these modifications impact signaling brain-wide. Ultimately, the McGovern team hopes not just to understand the craving state, but to find a way to relieve it — for good.

“If we can understand the craving state and correct it, or at least relieve a little bit of the pressure,” explains Wang, who will help lead the addiction initiative, “then maybe we can at least give people a chance to use their top-down control to not take the drug.”

The craving cycle

For individuals suffering from substance use disorders, craving fuels a cyclical pattern of escalating drug use. Following the euphoria induced by a drug like heroin or cocaine, depression sets in, accompanied by a drug craving motivated by the desire to relieve that suffering. And as addiction progresses, the peaks and valleys of this cycle dip lower: the pleasant feelings evoked by the drug become weaker, while the negative effects a person experiences in its absence worsen. The craving remains, and increasing use of the drug are required to relieve it.

By the time addiction sets in, the brain has been altered in ways that go beyond a drug’s immediate effects on neural signaling.

These insidious changes leave individuals susceptible to craving — and the vulnerable state endures. Long after the physical effects of withdrawal have subsided, people with substance use disorders can find their craving returns, triggered by exposure to a small amount of the drug, physical or social cues associated with previous drug use, or stress. So researchers will need to determine not only how different parts of the brain interact with one another during craving and how individual cells and the molecules within them are affected by the craving state — but also how things change as addiction develops and progresses.

Circuits, chemistry and connectivity

One clear starting point is the circuitry the brain uses to control motivation. Thanks in part to decades of research in the lab of McGovern Investigator Ann Graybiel, neuroscientists know a great deal about how these circuits learn which actions lead to pleasure and which lead to pain, and how they use that information to establish habits and evaluate the costs and benefits of complex decisions.

Graybiel’s work has shown that drugs of abuse strongly activate dopamine-responsive neurons in a part of the brain called the striatum, whose signals promote habit formation. By increasing the amount of dopamine that neurons release, these drugs motivate users to prioritize repeated drug use over other kinds of rewards, and to choose the drug in spite of pain or other negative effects. Her group continues to investigate the naturally occurring molecules that control these circuits, as well as how they are hijacked by drugs of abuse.

Distribution of opioid receptors targeted by morphine (shown in blue) in two regions in the dorsal striatum and nucleus accumbens of the mouse brain. Image: Ann Graybiel

In Fan Wang’s lab, work investigating the neural circuits that mediate the perception of physical pain has led her team to question the role of emotional pain in craving. As they investigated the source of pain sensations in the brain, they identified neurons in an emotion-regulating center called the central amygdala that appear to suppress physical pain in animals. Now, Wang wants to know whether it might be possible to modulate neurons involved in emotional pain to ameliorate the negative state that provokes drug craving.

These animal studies will be key to identifying the cellular and molecular changes that set the brain up for recurring cravings. And as McGovern scientists begin to investigate what happens in the brains of rodents that have been trained to self-administer addictive drugs like fentanyl or cocaine, they expect to encounter tremendous complexity.

McGovern Associate Investigator Polina Anikeeva, whose lab has pioneered new technologies that will help the team investigate the full spectrum of changes that underlie craving, says it will be important to consider impacts on the brain’s chemistry, firing patterns, and connectivity. To that end, multifunctional research probes developed in her lab will be critical to monitoring and manipulating neural circuits in animal models.

Imaging technology developed by investigator Ed Boyden will also enable nanoscale protein visualization brain-wide. An important goal will be to identify a neural signature of the craving state. With such a signal, researchers can begin to explore how to shut off that craving — possibly by directly modulating neural signaling.

Targeted treatments

“One of the reasons to study craving is because it’s a natural treatment point,” says McGovern Associate Investigator Alan Jasanoff. “And the dominant kind of approaches that people in our team think about are approaches that relate to neural circuits — to the specific connections between brain regions and how those could be changed.” The hope, he explains, is that it might be possible to identify a brain region whose activity is disrupted during the craving state, then use clinical brain stimulation methods to restore normal signaling — within that region, as well as in other connected parts of the brain.

To identify the right targets for such a treatment, it will be crucial to understand how the biology uncovered in laboratory animals reflects what’s happens in people with substance use disorders. Functional imaging in John Gabrieli’s lab can help bridge the gap between clinical and animal research by revealing patterns of brain activity associated with the craving state in both humans and rodents. A new technique developed in Jasanoff’s lab makes it possible to focus on the activity between specific regions of an animal’s brain. “By doing that, we hope to build up integrated models of how information passes around the brain in craving states, and of course also in control states where we’re not experiencing craving,” he explains.

In delving into the biology of the craving state, McGovern scientists are embarking on largely unexplored territory — and they do so with both optimism and urgency. “It’s hard to not appreciate just the size of the problem, and just how devastating addiction is,” says Anikeeva. “At this point, it just seems almost irresponsible to not work on it, especially when we do have the tools and we are interested in the general brain regions that are important for that problem. I would say that there’s almost a civic duty.”

Perfecting pitch perception

by Jennifer Michalowski |

New research from MIT neuroscientists suggest that natural soundscapes have shaped our sense of hearing, optimizing it for the kinds of sounds we most often encounter.

Mark Saddler, graduate fellow of the K. Lisa Yang Integrative Computational Neuroscience Center. Photo: Caitlin Cunningham

In a study reported December 14 in the journal Nature Communications, researchers led by McGovern Institute Associate Investigator Josh McDermott used computational modeling to explore factors that influence how humans hear pitch. Their model’s pitch perception closely resembled that of humans—but only when it was trained using music, voices, or other naturalistic sounds.

Humans’ ability to recognize pitch—essentially, the rate at which a sound repeats—gives melody to music and nuance to spoken language. Although this is arguably the best-studied aspect of human hearing, researchers are still debating which factors determine the properties of pitch perception, and why it is more acute for some types of sounds than others. McDermott, who is also an associate professor in MIT’s Department of Brain and Cognitive Sciences and an investigator with the Center for Brains Minds and Machines (CBMM), is particularly interested in understanding how our nervous system perceives pitch because cochlear implants, which send electrical signals about sound to the brain in people with profound deafness, don’t replicate this aspect of human hearing very well.

“Cochlear implants can do a pretty good job of helping people understand speech, especially if they’re in a quiet environment. But they really don’t reproduce the percept of pitch very well,” says Mark Saddler, a CBMM graduate student who co-led the project and an inaugural graduate fellow of the K. Lisa Yang Integrative Computational Neuroscience Center. “One of the reasons it’s important to understand the detailed basis of pitch perception in people with normal hearing is to try to get better insights into how we would reproduce that artificially in a prosthesis.”

Artificial hearing

Pitch perception begins in the cochlea, the snail-shaped structure in the inner ear where vibrations from sounds are transformed into electrical signals and relayed to the brain via the auditory nerve. The cochlea’s structure and function help determine how and what we hear. And although it hasn’t been possible to test this idea experimentally, McDermott’s team suspected our “auditory diet” might shape our hearing as well.

To explore how both our ears and our environment influence pitch perception, McDermott, Saddler and research assistant Ray Gonzalez built a computer model called a deep neural network. Neural networks are a type of machine learning model widely used in automatic speech recognition and other artificial intelligence applications. Although the structure of an artificial neural network coarsely resembles the connectivity of neurons in the brain, the models used in engineering applications don’t actually hear the same way humans do—so the team developed a new model to reproduce human pitch perception. Their approach combined an artificial neural network with an existing model of the mammalian ear, uniting the power of machine learning with insights from biology. “These new machine learning models are really the first that can be trained to do complex auditory tasks and actually do them well, at human levels of performance,” Saddler explains.

The researchers trained the neural network to estimate pitch by asking it to identify the repetition rate of sounds in a training set. This gave them the flexibility to change the parameters under which pitch perception developed. They could manipulate the types of sound they presented to the model, as well as the properties of the ear that processed those sounds before passing them on to the neural network.

When the model was trained using sounds that are important to humans, like speech and music, it learned to estimate pitch much as humans do. “We very nicely replicated many characteristics of human perception…suggesting that it’s using similar cues from the sounds and the cochlear representation to do the task,” Saddler says.

But when the model was trained using more artificial sounds or in the absence of any background noise, its behavior was very different. For example, Saddler says, “If you optimize for this idealized world where there’s never any competing sources of noise, you can learn a pitch strategy that seems to be very different from that of humans, which suggests that perhaps the human pitch system was really optimized to deal with cases where sometimes noise is obscuring parts of the sound.”

The team also found the timing of nerve signals initiated in the cochlea to be critical to pitch perception. In a healthy cochlea, McDermott explains, nerve cells fire precisely in time with the sound vibrations that reach the inner ear. When the researchers skewed this relationship in their model, so that the timing of nerve signals was less tightly correlated to vibrations produced by incoming sounds, pitch perception deviated from normal human hearing. 

McDermott says it will be important to take this into account as researchers work to develop better cochlear implants. “It does very much suggest that for cochlear implants to produce normal pitch perception, there needs to be a way to reproduce the fine-grained timing information in the auditory nerve,” he says. “Right now, they don’t do that, and there are technical challenges to making that happen—but the modeling results really pretty clearly suggest that’s what you’ve got to do.”