Finding the brain’s compass

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

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

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

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

Schooling fish

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

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

Simple and persistent ring

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

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

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

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

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

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

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

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

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

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

Shaping the future

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

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

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

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

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

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

Ila Fiete joins the McGovern Institute

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

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

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

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

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

Ila Fiete

Neural Coding and Dynamics

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

School of Science welcomes 10 professors

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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