Researcher: Ila Fiete

International neuroscience collaboration unveils comprehensive cellular-resolution map of brain activity

by Julie Pryor |

The first comprehensive map of mouse brain activity has been unveiled by a large international collaboration of neuroscientists. Researchers from the International Brain Laboratory (IBL), including McGovern Investigator Ila Fiete, published their findings today in two papers in Nature, revealing insights into how decision-making unfolds across the entire brain in mice at single-cell resolution. This brain-wide activity map challenges the traditional hierarchical view of information processing in the brain and shows that decision-making is distributed across many regions in a highly coordinated way.

“This is the first time anyone has produced a full, brain-wide map of the activity of single neurons during decision-making,” explains Co-Founder of IBL Alexandre Pouget. “The scale is unprecedented as we recorded from over half a million neurons across mice in 12 labs, covering 279 brain areas, which together represent 95% of the mouse brain volume. The decision-making activity, and particularly reward, lit up the brain like a Christmas tree,” adds Pouget, who is also a Group Leader at the University of Geneva.

Brain-wide map showing 75,000 analyzed neurons lighting up during different stages of decision-making. At the beginning of the trial, the activity is quiet. Then it builds up in the visual areas at the back of the brain, followed by a rise in activity spreading across the brain as evidence accumulates towards a decision. Next, motor areas light up as there is movement onset and finally there is a spike in activity everywhere in the brain as the animal is rewarded.

Modeling decision-making

The brain map was made possible by a major international collaboration of neuroscientists from multiple universities, including MIT. Researchers across 12 labs used state-of-the-art silicon electrodes, called Neuropixels probes,  for simultaneous neural recordings to measure brain activity while mice were carrying out a decision-making task.

McGovern Associate Investigator Ila Fiete. Photo: Caitlin Cunningham

“Participating in the International Brain Laboratory has added new ways for our group to contribute to science,” says Fiete, who is also a professor of brain and cognitive sciences director of the K. Lisa Yang ICoN Center at MIT. “Our lab has helped standardize methods to analyze and generate robust conclusions from data. As computational neuroscientists interested in building models of how the brain works, access to brainwide recordings is incredible: the traditional approach of recording from one or a few brain areas limited our ability to build and test theories, resulting in fragmented models. Now we have the delightful but formidable task to make sense of how all parts of the brain coordinate to perform a behavior. Surprisingly, having a full view of the brain leads to simplifications in the models of decision making.”

The labs collected data from mice performing a decision-making task with sensory, motor, and cognitive components. In the task, a mouse sits in front of a screen and a light appears on the left or right side. If the mouse then responds by moving a small wheel in the correct direction, it receives a reward.

In some trials, the light is so faint that the animal must guess which way to turn the wheel, for which it can use prior knowledge: the light tends to appear more frequently on one side for a number of trials, before the high-frequency side switches. Well-trained mice learn to use this information to help them make correct guesses. These challenging trials therefore allowed the researchers to study how prior expectations influence perception and decision-making.

Brain-wide results

The first paper, “A brain-wide map of neural activity during complex behaviour,” showed that decision-making signals are surprisingly distributed across the brain, not localized to specific regions. This adds brain-wide evidence to a growing number of studies that challenge the traditional hierarchical model of brain function and emphasizes that there is constant communication across brain areas during decision-making, movement onset, and even reward. This means that neuroscientists will need to take a more holistic, brain-wide approach when studying complex behaviors in future.

Flat maps of the mouse brain showing which areas have significant changes in activity during each of three task intervals. Credit: Michael Schartner & International Brain Laboratory

“The unprecedented breadth of our recordings pulls back the curtain on how the entire brain performs the whole arc of sensory processing, cognitive decision-making, and movement generation,” says Fiete. “Structuring a collaboration that collects a large standardized dataset which single labs could not assemble is a revolutionary new direction for systems neuroscience, initiating the field into the hyper-collaborative mode that has contributed to leaps forward in particle physics and human genetics. Beyond our own conclusions, the dataset and associated technologies, which were released much earlier as part of the IBL mission, have already become a massively used resource for the entire neuroscience community.”

The second paper, “Brain-wide representations of prior information,” showed that prior expectations, our beliefs about what is likely to happen based on our recent experience, are encoded throughout the brain. Surprisingly, these expectations are not only found in cognitive areas, but also brain areas that process sensory information and control actions. For example, expectations are even encoded in early sensory areas such as the thalamus, the brain’s first relay for visual input from the eye. This supports the view that the brain acts as a prediction machine, but with expectations encoded across multiple brain structures playing a central role in guiding behavior responses. These findings could have implications for understanding conditions such as schizophrenia and autism, which are thought to be caused by differences in the way expectations are updated in the brain.

“Much remains to be unpacked: if it is possible to find a signal in a brain area, does it mean that this area is generating the signal, or simply reflecting a signal generated somewhere else? How strongly is our perception of the world is shaped by our expectations? Now we can generate some quantitative answers and begin the next phase experiments to learn about the origins of the expectation signals by intervening to modulate their activity,” says Fiete.

Looking ahead, the team at IBL plan to expand beyond their initial focus on decision-making to explore a broader range of neuroscience questions. With renewed funding in hand, IBL aims to expand its research scope and continue to support large-scale, standardized experiments.

New model of collaborative neuroscience

Officially launched in 2017, IBL introduced a new model of collaboration in neuroscience that uses a standardized set of tools and data processing pipelines shared across multiple labs, enabling the collection of massive datasets while ensuring data alignment and reproducibility. This approach to democratize and accelerate science draws inspiration from large-scale collaborations in physics and biology, such as CERN and the Human Genome Project.

All data from these studies, along with detailed specifications of the tools and protocols used for data collection, are openly accessible to the global scientific community for further analysis and research. Summaries of these resources can be viewed and downloaded on the IBL website under the sections: Data, Tools, Protocols.

This research was supported by grants from Wellcome (209558 and 216324), the Simons Foundation, The National Institutes of Health (NIH U19NS12371601), the National Science Foundation (NSF 1707398), the Gatsby Charitable Foundation (GAT3708), andby the Max Planck Society and the Humboldt Foundation.

 

How nature organizes itself, from brain cells to ecosystems

by McGovern Institute |

McGovern Associate Investigator Ila Fiete. Photo: Caitlin Cunningham

Look around, and you’ll see it everywhere: the way trees form branches, the way cities divide into neighborhoods, the way the brain organizes into regions. Nature loves modularity—a limited number of self-contained units that combine in different ways to perform many functions. But how does this organization arise? Does it follow a detailed genetic blueprint, or can these structures emerge on their own?

A new study from McGovern Associate Investigator Ila Fiete suggests a surprising answer.

In findings published today in Nature, Fiete, a professor of brain and cognitive sciences and director of the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, reports that a mathematical model called peak selection can explain how modules emerge without strict genetic instructions. Her team’s findings, which apply to brain systems and ecosystems, help explain how modularity occurs across nature, no matter the scale.

Joining two big ideas

“Scientists have debated how modular structures form. One hypothesis suggests that various genes are turned on at different locations to begin or end a structure. This explains how insect embryos develop body segments, with genes turning on or off at specific concentrations of a smooth chemical gradient in the insect egg,” says Fiete, who is the senior author of the paper. Mikail Khona, a former graduate student and K. Lisa Yang ICoN Center Graduate Fellow, and postdoctoral associate Sarthak Chandra also led the study.

Another idea, inspired by mathematician Alan Turing, suggests that a structure could emerge from competition—small-scale interactions can create repeating patterns, like the spots on a cheetah or the ripples in sand dunes.

Both ideas work well in some cases, but fail in others. The new research suggests that nature need not pick one approach over the other. The authors propose a simple mathematical principle called peak selection, showing that when a smooth gradient is paired with local interactions that are competitive, modular structures emerge naturally. “In this way, biological systems can organize themselves into sharp modules without detailed top-down instruction,” says Chandra.

Modular systems in the brain

The researchers tested their idea on grid cells, which play a critical role in spatial navigation as well as the storage of episodic memories. Grid cells fire in a repeating triangular pattern as animals move through space, but they don’t all work at the same scale—they are organized into distinct modules, each responsible for mapping space at slightly different resolutions.

A visual depiction of two different modules in grid cells, used to map space at slightly different resolutions. Image: Fiete Lab

No one knows how these modules form, but Fiete’s model shows that gradual variations in cellular properties along one dimension in the brain, combined with local neural interactions, could explain the entire structure. The grid cells naturally sort themselves into distinct groups with clear boundaries, without external maps or genetic programs telling them where to go. “Our work explains how grid cell modules could emerge. The explanation tips the balance toward the possibility of self-organization. It predicts that there might be no gene or intrinsic cell property that jumps when the grid cell scale jumps to another module,” notes Khona.

Modular systems in nature

The same principle applies beyond neuroscience. Imagine a landscape where temperatures and rainfall vary gradually over a space. You might expect species to be spread and also vary smoothly over this region. But in reality, ecosystems often form species clusters with sharp boundaries—distinct ecological “neighborhoods” that don’t overlap.

Fiete’s study suggests why: Local competition, cooperation, and predation between species interact with the global environmental gradients to create natural separations, even when the underlying conditions change gradually. This phenomenon can be explained using peak selection—and suggests that the same principle that shapes brain circuits could also be at play in forests and oceans.

A self-organizing world

One of the researchers’ most striking findings is that modularity in these systems is remarkably robust. Change the size of the system, and the number of modules stays the same, they just scale up or down. That means a mouse brain and a human brain could use the same fundamental rules to form their navigation circuits, just at different sizes.

The model also makes testable predictions. If it’s correct, grid cell modules should follow simple spacing ratios. In ecosystems, species distributions should form distinct clusters even without sharp environmental shifts.

Fiete notes that their work adds another conceptual framework to biology. “Peak selection can inform future experiments, not only in grid cell research but across developmental biology.”

How one brain circuit encodes memories of both places and events

Anne Trafton |

Nearly 50 years ago, neuroscientists discovered cells within the brain’s hippocampus that store memories of specific locations. These cells also play an important role in storing memories of events, known as episodic memories. While the mechanism of how place cells encode spatial memory has been well-characterized, it has remained a puzzle how they encode episodic memories.

A new model developed by MIT researchers explains how those place cells can be recruited to form episodic memories, even when there’s no spatial component. According to this model, place cells, along with grid cells found in the entorhinal cortex, act as a scaffold that can be used to anchor memories as a linked series.

“This model is a first-draft model of the entorhinal-hippocampal episodic memory circuit. It’s a foundation to build on to understand the nature of episodic memory. That’s the thing I’m really excited about,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

The model accurately replicates several features of biological memory systems, including the large storage capacity, gradual degradation of older memories, and the ability of people who compete in memory competitions to store enormous amounts of information in “memory palaces.”

MIT Research Scientist Sarthak Chandra and Sugandha Sharma PhD ’24 are the lead authors of the study, which appears today in Nature. Rishidev Chaudhuri, an assistant professor at the University of California at Davis, is also an author of the paper.

An index of memories

To encode spatial memory, place cells in the hippocampus work closely with grid cells — a special type of neuron that fires at many different locations, arranged geometrically in a regular pattern of repeating triangles. Together, a population of grid cells forms a lattice of triangles representing a physical space.

In addition to helping us recall places where we’ve been, these hippocampal-entorhinal circuits also help us navigate new locations. From human patients, it’s known that these circuits are also critical for forming episodic memories, which might have a spatial component but mainly consist of events, such as how you celebrated your last birthday or what you had for lunch yesterday.

“The same hippocampal and entorhinal circuits are used not just for spatial memory, but also for general episodic memory,” says Fiete, who is also the director of the K. Lisa Yang ICoN Center at MIT. “The question you can ask is what is the connection between spatial and episodic memory that makes them live in the same circuit?”

Two hypotheses have been proposed to account for this overlap in function. One is that the circuit is specialized to store spatial memories because those types of memories — remembering where food was located or where predators were seen — are important to survival. Under this hypothesis, this circuit encodes episodic memories as a byproduct of spatial memory.

An alternative hypothesis suggests that the circuit is specialized to store episodic memories, but also encodes spatial memory because location is one aspect of many episodic memories.

In this work, Fiete and her colleagues proposed a third option: that the peculiar tiling structure of grid cells and their interactions with hippocampus are equally important for both types of memory — episodic and spatial. To develop their new model, they built on computational models that her lab has been developing over the past decade, which mimic how grid cells encode spatial information.

“We reached the point where I felt like we understood on some level the mechanisms of the grid cell circuit, so it felt like the time to try to understand the interactions between the grid cells and the larger circuit that includes the hippocampus,” Fiete says.

In the new model, the researchers hypothesized that grid cells interacting with hippocampal cells can act as a scaffold for storing either spatial or episodic memory. Each activation pattern within the grid defines a “well,” and these wells are spaced out at regular intervals. The wells don’t store the content of a specific memory, but each one acts as a pointer to a specific memory, which is stored in the synapses between the hippocampus and the sensory cortex.

When the memory is triggered later from fragmentary pieces, grid and hippocampal cell interactions drive the circuit state into the nearest well, and the state at the bottom of the well connects to the appropriate part of the sensory cortex to fill in the details of the memory. The sensory cortex is much larger than the hippocampus and can store vast amounts of memory.

“Conceptually, we can think about the hippocampus as a pointer network. It’s like an index that can be pattern-completed from a partial input, and that index then points toward sensory cortex, where those inputs were experienced in the first place,” Fiete says. “The scaffold doesn’t contain the content, it only contains this index of abstract scaffold states.”

Furthermore, events that occur in sequence can be linked together: Each well in the grid cell-hippocampal network efficiently stores the information that is needed to activate the next well, allowing memories to be recalled in the right order.

Modeling memory cliffs and palaces

The researchers’ new model replicates several memory-related phenomena much more accurately than existing models that are based on Hopfield networks — a type of neural network that can store and recall patterns.

While Hopfield networks offer insight into how memories can be formed by strengthening connections between neurons, they don’t perfectly model how biological memory works. In Hopfield models, every memory is recalled in perfect detail until capacity is reached. At that point, no new memories can form, and worse, attempting to add more memories erases all prior ones. This “memory cliff” doesn’t accurately mimic what happens in the biological brain, which tends to gradually forget the details of older memories while new ones are continually added.

The new MIT model captures findings from decades of recordings of grid and hippocampal cells in rodents made as the animals explore and forage in various environments. It also helps to explain the underlying mechanisms for a memorization strategy known as a memory palace. One of the tasks in memory competitions is to memorize the shuffled sequence of cards in one or several card decks. They usually do this by assigning each card to a particular spot in a memory palace — a memory of a childhood home or other environment they know well. When they need to recall the cards, they mentally stroll through the house, visualizing each card in its spot as they go along. Counterintuitively, adding the memory burden of associating cards with locations makes recall stronger and more reliable.

The MIT team’s computational model was able to perform such tasks very well, suggesting that memory palaces take advantage of the memory circuit’s own strategy of associating inputs with a scaffold in the hippocampus, but one level down: Long-acquired memories reconstructed in the larger sensory cortex can now be pressed into service as a scaffold for new memories. This allows for the storage and recall of many more items in a sequence than would otherwise be possible.

The researchers now plan to build on their model to explore how episodic memories could become converted to cortical “semantic” memory, or the memory of facts dissociated from the specific context in which they were acquired (for example, Paris is the capital of France), how episodes are defined, and how brain-like memory models could be integrated into modern machine learning.

The research was funded by the U.S. Office of Naval Research, the National Science Foundation under the Robust Intelligence program, the ARO-MURI award, the Simons Foundation, and the K. Lisa Yang ICoN Center.

Finding the way

by Jennifer Michalowski |

This story also appears in the Fall 2024 issue of BrainScan.

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When you arrive in a new city, every outing can be an exploration. You may know your way to a few places, but only if you follow a specific route. As you wander around a bit, get lost a few times, and familiarize yourself with some landmarks and where they are relative to each other, your brain develops a cognitive map of the space. You learn how things are laid out, and navigating gets easier.

It takes a lot to generate a useful mental map. “You have to understand the structure of relationships in the world,” says McGovern Investigator Mehrdad Jazayeri. “You need learning and experience to construct clever representations. The advantage is that when you have them, the world is an easier place to deal with.”

Indeed, Jazayeri says, internal models like these are the core of intelligent behavior.

Mehrdad Jazayeri (right) and graduate student Jack Gabel sit inside a rig designed to probe the brain’s ability to solve real-world problems with internal models. Photo: Steph Stevens

Many McGovern scientists see these cognitive maps as windows into their biggest questions about the brain: how it represents the external world, how it lets us learn and adapt, and how it forms and reconstructs memories. Researchers are learning that cells and strategies that the brain uses to understand the layout of a space also help track other kinds of structures in the world, too — from variations in sound to sequences of events. By studying how neurons behave as animals navigate their environments, McGovern researchers also expect to deepen their understanding of other important cognitive functions as well.

Decoding spatial maps

McGovern Investigator Ila Fiete builds theoretical models that help explain how spatial maps are formed in the brain. Previous research has shown that “place cells” and “grid cells” are place-sensitive neurons in the brain’s hippocampus and entorhinal cortex whose firing patterns help an animal map out a space. As an animal becomes familiar with its environment, subsets of these cells become tied to specific locations, firing only when the animal is in them.

Microscopic image of the mouse hippocampus
The brain’s ability to navigate the world is made possible by a brain circuit that includes the hippocampus (above), entorhinal cortex, and retrosplenial cortex. The firing pattern of “grid cells” and “place cells” in this circuit help form mental representations, or cognitive maps, of the external world. These brain regions are also among the first areas to be affected in people with Alzheimer’s, who often have trouble navigating. Image: Qian Chen, Guoping Feng

Fiete’s models have shown how these circuits can integrate information about movement, like signals from the muscles and vestibular system that change as an animal moves around, to calculate and update its estimate of an animal’s position in space. Fiete suspects the cells that do this can use the same strategy to keep track of other kinds of movement or change.

Mapping a space is about understanding where things are in relationship to one another, says Jazayeri, and tracking relationships is useful for modeling many kinds of structure in the world. For example, the hippocampus and entorhinal cortex are also closely linked to episodic memory, which keeps track of the connections between events and experiences.

“These brain areas are thought to be critical for learning relationships,” Jazayeri says.

Navigating virtual worlds

A key feature of cognitive maps is that they enable us to make predictions and respond to new situations without relying on immediate sensory cues. In a study published in Nature this June, Jazayeri and Fiete saw evidence of the brain’s ability to call up an internal model of an abstract domain: they watched neurons in the brain’s entorhinal cortex register a sequence of images, even when they were hidden from view.

Two scientists write equations on a glass wall with a marker.
Ila Fiete and postdoc Sarthak Chandra (right) develop theoretical models to study the brain. Photo: Steph Stevens

We can remember the layout of our home from far away or plan a walk through the neighborhood without stepping outside — so it may come as no surprise that the brain can call up its internal model in the absence of movement or sensory inputs. Indeed, previous research has shown that the circuits that encode physical space also encode abstract spaces like auditory sound sequences. But these experiments were performed in the presence of the stimuli, and Jazayeri and his team wanted to know whether simply imagining movement through an abstract domain may also evoke the same cognitive maps.

To test the entorhinal cortex’s ability to do this, Jazayeri and his team designed an experiment where animals had to “mentally” navigate through a previously explored, but now invisible, sequence of images. Working with Fiete, they found that the neurons that had become responsive to particular images in the visible sequence would also fire when mentally navigating the sequence in which images were hidden from view — suggesting the animal was conjuring a representation of the image in its mind.

Colored dots in the shape of a ring.
Ila Fiete has shown that the brain generates a one-dimensional ring of neural activity that acts as a compass. Here, head direction is indicated by color. Image: Ila Fiete

“You see these neurons in the entorhinal cortex undergo very clear dynamic patterns that are in correspondence with what we think the animal might be thinking at the time,” Jazayeri says. “They are updating themselves without any change out there in the world.”

The team then incorporated their data into a computational model to explore how neural circuits might form a mental model of abstract sequences. Their artificial circuit showed that the external inputs (eg., image sequences) become associated with internal models through a simple associative learning rule in which neurons that fire together, wire together. This model suggests that imagined movement could update the internal representations, and the learned association of these internal representations with external inputs might enable a recall of the corresponding inputs even when they are absent.

More broadly, Fiete’s research on cognitive mapping in the hippocampus is leading to some interesting predictions: “One of the conclusions we’re coming to in my group is that when you reconstruct a memory, the area that’s driving that reconstruction is the entorhinal cortex and hippocampus but the reconstruction may happen in the sensory periphery, using the representations that played a role in experiencing that stimulus in the first place,” Fiete explains. “So when I reconstruct an image, I’m likely using my visual cortex to do that reconstruction, driven by the hippocampal complex.” Signals from the entorhinal cortex to the visual cortex during navigation could help an animal visualize landmarks and find its way, even when those landmarks are not visible in the external world.

Landmark coding

Near the entorhinal cortex is the retrosplenial cortex, another brain area that seems to be important for navigation. It is positioned to integrate visual signals with information about the body’s position and movement through space. Both the retrosplenial cortex and the entorhinal cortex are among the first areas impacted by Alzheimer’s disease; spatial disorientation and navigation difficulties may be consequences of their degeneration.

Researchers suspect the retrosplenial cortex may be key to letting an animal know not just where something is, but also how to get there. McGovern Investigator Mark Harnett explains that to generate a cognitive map that can be used to navigate, an animal must understand not just where objects or other cues are in relationship to itself, but also where they are in relationship to each other.

In a study reported in eLife in 2020, Harnett and colleagues may have glimpsed both of these kinds of representations of space inside the brain. They watched neurons there light up as mice ran on a treadmill and tracked the passage of a virtual environment. As the mice became familiar with the landscape and learned where they were likely to find a reward, activity in the retrosplenial cortex changed.

A scientist looks at a computer monitor and adjusts a small wheel.
Lukas Fischer, a Harnett lab postdoc, operates a rig designed to study how mice navigate a virtual environment. Photo: Justin Knight

“What we found was this representation started off sort of crude and mostly about what the animal was doing. And then eventually it became more about the task, the landscape, and the reward,” Harnett says.

Harnett’s team has since begun investigating how the retrosplenial cortex enables more complex spatial reasoning. They designed an experiment in which mice must understand many spatial relationships to access a treat. The experimental setup requires mice to consider the location of reward ports, the center of their environment, and their own viewing angle. Most of the time, they succeed. “They have to really do some triangulation, and the retrosplenial cortex seems to be critical for that,” Harnett says.

When the team monitored neural activity during the task, they found evidence that when an animal wasn’t quite sure where to go, its brain held on to multiple spatial hypotheses at the same time, until new information ruled one out.

Fiete, who has worked with Harnett to explore how neural circuits can execute this kind of spatial reasoning, points out that Jazayeri’s team has observed similar reasoning in animals that must make decisions based on temporarily ambiguous auditory cues. “In both cases, animals are able to hold multiple hypotheses in mind and do the inference,” she says. “Mark’s found that the retrosplenial cortex contains all the signals necessary to do that reasoning.”

Beyond spatial reasoning

As his team learns more about the how the brain creates and uses cognitive maps, Harnett hopes activity in the retrosplenial cortex will shed light on a fundamental aspect of the brain’s organization. The retrosplenial cortex doesn’t just receive information from the brain’s vision-processing center, it also sends signals back. He suspects these may direct the visual cortex to relay information that is particularly pertinent to forming or using a meaningful cognitive map.

“The brain’s navigation system is a beautiful playground.” – Ila Fiete

This kind of connectivity, where parts of the brain that carry out complex cognitive processing send signals back to regions that handle simpler functions, is common in the brain. Figuring out why is a key pursuit in Harnett’s lab. “I want to use that as a model for thinking about the larger cortical computations, because you see this kind of motif repeated in a lot of ways, and it’s likely key for understanding how learning works,” he says.

Fiete is particularly interested in unpacking the common set of principles that allow cell circuits to generate maps of both our physical environment and our abstract experiences. What is it about this set of brain areas and circuits that, on the one hand, permits specific map-building computations, and, on the other hand, generalizes across physical space and abstract experience?

“The brain’s navigation system is a beautiful playground,” she says, “and an amazing system in which to investigate all of these questions.”

Study reveals how an anesthesia drug induces unconsciousness

Anne Trafton |

There are many drugs that anesthesiologists can use to induce unconsciousness in patients. Exactly how these drugs cause the brain to lose consciousness has been a longstanding question, but MIT neuroscientists have now answered that question for one commonly used anesthesia drug.

Using a novel technique for analyzing neuron activity, the researchers discovered that the drug propofol induces unconsciousness by disrupting the brain’s normal balance between stability and excitability. The drug causes brain activity to become increasingly unstable, until the brain loses consciousness.

“The brain has to operate on this knife’s edge between excitability and chaos.” – Earl K. Miller

“It’s got to be excitable enough for its neurons to influence one another, but if it gets too excitable, it spins off into chaos. Propofol seems to disrupt the mechanisms that keep the brain in that narrow operating range,” says Earl K. Miller, the Picower Professor of Neuroscience and a member of MIT’s Picower Institute for Learning and Memory.

The new findings, reported today in Neuron, could help researchers develop better tools for monitoring patients as they undergo general anesthesia.

Miller and Ila Fiete, a professor of brain and cognitive sciences, the director of the K. Lisa Yang Integrative Computational Neuroscience Center (ICoN), and a member of MIT’s McGovern Institute for Brain Research, are the senior authors of the new study. MIT graduate student Adam Eisen and MIT postdoc Leo Kozachkov are the lead authors of the paper.

Losing consciousness

Propofol is a drug that binds to GABA receptors in the brain, inhibiting neurons that have those receptors. Other anesthesia drugs act on different types of receptors, and the mechanism for how all of these drugs produce unconsciousness is not fully understood.

Miller, Fiete, and their students hypothesized that propofol, and possibly other anesthesia drugs, interfere with a brain state known as “dynamic stability.” In this state, neurons have enough excitability to respond to new input, but the brain is able to quickly regain control and prevent them from becoming overly excited.

Woman gestures with her hand in front of a glass wall with equations written on it.
Ila Fiete in her lab at the McGovern Institute. Photo: Steph Stevens

Previous studies of how anesthesia drugs affect this balance have found conflicting results: Some suggested that during anesthesia, the brain shifts toward becoming too stable and unresponsive, which leads to loss of consciousness. Others found that the brain becomes too excitable, leading to a chaotic state that results in unconsciousness.

Part of the reason for these conflicting results is that it has been difficult to accurately measure dynamic stability in the brain. Measuring dynamic stability as consciousness is lost would help researchers determine if unconsciousness results from too much stability or too little stability.

In this study, the researchers analyzed electrical recordings made in the brains of animals that received propofol over an hour-long period, during which they gradually lost consciousness. The recordings were made in four areas of the brain that are involved in vision, sound processing, spatial awareness, and executive function.

These recordings covered only a tiny fraction of the brain’s overall activity, so to overcome that, the researchers used a technique called delay embedding. This technique allows researchers to characterize dynamical systems from limited measurements by augmenting each measurement with measurements that were recorded previously.

Using this method, the researchers were able to quantify how the brain responds to sensory inputs, such as sounds, or to spontaneous perturbations of neural activity.

In the normal, awake state, neural activity spikes after any input, then returns to its baseline activity level. However, once propofol dosing began, the brain started taking longer to return to its baseline after these inputs, remaining in an overly excited state. This effect became more and more pronounced until the animals lost consciousness.

This suggests that propofol’s inhibition of neuron activity leads to escalating instability, which causes the brain to lose consciousness, the researchers say.

Better anesthesia control

To see if they could replicate this effect in a computational model, the researchers created a simple neural network. When they increased the inhibition of certain nodes in the network, as propofol does in the brain, network activity became destabilized, similar to the unstable activity the researchers saw in the brains of animals that received propofol.

“We looked at a simple circuit model of interconnected neurons, and when we turned up inhibition in that, we saw a destabilization. So, one of the things we’re suggesting is that an increase in inhibition can generate instability, and that is subsequently tied to loss of consciousness,” Eisen says.

As Fiete explains, “This paradoxical effect, in which boosting inhibition destabilizes the network rather than silencing or stabilizing it, occurs because of disinhibition. When propofol boosts the inhibitory drive, this drive inhibits other inhibitory neurons, and the result is an overall increase in brain activity.”

The researchers suspect that other anesthetic drugs, which act on different types of neurons and receptors, may converge on the same effect through different mechanisms — a possibility that they are now exploring.

If this turns out to be true, it could be helpful to the researchers’ ongoing efforts to develop ways to more precisely control the level of anesthesia that a patient is experiencing. These systems, which Miller is working on with Emery Brown, the Edward Hood Taplin Professor of Medical Engineering at MIT, work by measuring the brain’s dynamics and then adjusting drug dosages accordingly, in real-time.

“If you find common mechanisms at work across different anesthetics, you can make them all safer by tweaking a few knobs, instead of having to develop safety protocols for all the different anesthetics one at a time,” Miller says. “You don’t want a different system for every anesthetic they’re going to use in the operating room. You want one that’ll do it all.”

The researchers also plan to apply their technique for measuring dynamic stability to other brain states, including neuropsychiatric disorders.

“This method is pretty powerful, and I think it’s going to be very exciting to apply it to different brain states, different types of anesthetics, and also other neuropsychiatric conditions like depression and schizophrenia,” Fiete says.

The research was funded by the Office of Naval Research, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, the National Science Foundation Directorate for Computer and Information Science and Engineering, the Simons Center for the Social Brain, the Simons Collaboration on the Global Brain, the JPB Foundation, the McGovern Institute, and the Picower Institute.

Just thinking about a location activates mental maps in the brain

Anne Trafton |

As you travel your usual route to work or the grocery store, your brain engages cognitive maps stored in your hippocampus and entorhinal cortex. These maps store information about paths you have taken and locations you have been to before, so you can navigate whenever you go there.

New research from MIT has found that such mental maps also are created and activated when you merely think about sequences of experiences, in the absence of any physical movement or sensory input. In an animal study, the researchers found that the entorhinal cortex harbors a cognitive map of what animals experience while they use a joystick to browse through a sequence of images. These cognitive maps are then activated when thinking about these sequences, even when the images are not visible.

This is the first study to show the cellular basis of mental simulation and imagination in a nonspatial domain through activation of a cognitive map in the entorhinal cortex.

“These cognitive maps are being recruited to perform mental navigation, without any sensory input or motor output. We are able to see a signature of this map presenting itself as the animal is going through these experiences mentally,” says Mehrdad Jazayeri, 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.

McGovern Institute Research Scientist Sujaya Neupane is the lead author of the paper, which appears today in Nature. Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, and director of the K. Lisa Yang Integrative Computational Neuroscience Center, is also an author of the paper.

Mental maps

A great deal of work in animal models and humans has shown that representations of physical locations are stored in the hippocampus, a small seahorse-shaped structure, and the nearby entorhinal cortex. These representations are activated whenever an animal moves through a space that it has been in before, just before it traverses the space, or when it is asleep.

“Most prior studies have focused on how these areas reflect the structures and the details of the environment as an animal moves physically through space,” Jazayeri says. “When an animal moves in a room, its sensory experiences are nicely encoded by the activity of neurons in the hippocampus and entorhinal cortex.”

In the new study, Jazayeri and his colleagues wanted to explore whether these cognitive maps are also built and then used during purely mental run-throughs or imagining of movement through nonspatial domains.

To explore that possibility, the researchers trained animals to use a joystick to trace a path through a sequence of images (“landmarks”) spaced at regular temporal intervals. During the training, the animals were shown only a subset of pairs of images but not all the pairs. Once the animals had learned to navigate through the training pairs, the researchers tested if animals could handle the new pairs they had never seen before.

One possibility is that animals do not learn a cognitive map of the sequence, and instead solve the task using a memorization strategy. If so, they would be expected to struggle with the new pairs. Instead, if the animals were to rely on a cognitive map, they should be able to generalize their knowledge to the new pairs.

“The results were unequivocal,” Jazayeri says. “Animals were able to mentally navigate between the new pairs of images from the very first time they were tested. This finding provided strong behavioral evidence for the presence of a cognitive map. But how does the brain establish such a map?”

To address this question, the researchers recorded from single neurons in the entorhinal cortex as the animals performed this task. Neural responses had a striking feature: As the animals used the joystick to navigate between two landmarks, neurons featured distinctive bumps of activity associated with the mental representation of the intervening landmarks.

“The brain goes through these bumps of activity at the expected time when the intervening images would have passed by the animal’s eyes, which they never did,” Jazayeri says. “And the timing between these bumps, critically, was exactly the timing that the animal would have expected to reach each of those, which in this case was 0.65 seconds.”

The researchers also showed that the speed of the mental simulation was related to the animals’ performance on the task: When they were a little late or early in completing the task, their brain activity showed a corresponding change in timing. The researchers also found evidence that the mental representations in the entorhinal cortex don’t encode specific visual features of the images, but rather the ordinal arrangement of the landmarks.

A model of learning

To further explore how these cognitive maps may work, the researchers built a computational model to mimic the brain activity that they found and demonstrate how it could be generated. They used a type of model known as a continuous attractor model, which was originally developed to model how the entorhinal cortex tracks an animal’s position as it moves, based on sensory input.

The researchers customized the model by adding a component that was able to learn the activity patterns generated by sensory input. This model was then able to learn to use those patterns to reconstruct those experiences later, when there was no sensory input.

“The key element that we needed to add is that this system has the capacity to learn bidirectionally by communicating with sensory inputs. Through the associational learning that the model goes through, it will actually recreate those sensory experiences,” Jazayeri says.

The researchers now plan to investigate what happens in the brain if the landmarks are not evenly spaced, or if they’re arranged in a ring. They also hope to record brain activity in the hippocampus and entorhinal cortex as the animals first learn to perform the navigation task.

“Seeing the memory of the structure become crystallized in the mind, and how that leads to the neural activity that emerges, is a really valuable way of asking how learning happens,” Jazayeri says.

The research was funded by the Natural Sciences and Engineering Research Council of Canada, the Québec Research Funds, the National Institutes of Health, and the Paul and Lilah Newton Brain Science Award.

What is consciousness?

by Rubina Veerakone |

In the hit T.V. show “Westworld,” Dolores Abernathy, a golden-tressed belle, lives in the days when Manifest Destiny still echoed in America. She begins to notice unusual stirrings shaking up her quaint western town—and soon discovers that her skin is synthetic, and her mind, metal. She’s a cyborg meant to entertain humans. The key to her autonomy lies in reaching consciousness.

Shows like “Westworld” and other media probe the idea of consciousness, attempting to nail down a definition of the concept. However, though humans have ruminated on consciousness for centuries, we still don’t have a solid definition (even the Merriam-Webster dictionary lists five). One framework suggests that consciousness is any experience, from eating a candy bar to heartbreak. Another argues that it is how certain stimuli influence one’s behavior.

MIT graduate student Adam Eisen.

While some search for a philosophical explanation, MIT graduate student Adam Eisen seeks a scientific one.

Eisen studies consciousness in the labs of Ila Fiete, an associate investigator at the McGovern Institute, and Earl Miller, an investigator at the Picower Institute for Learning and Memory. His work melds seemingly opposite fields, using mathematical models to quantitatively explain, and thereby ground, the loftiness of consciousness.

In the Fiete lab, Eisen leverages computational methods to compare the brain’s electrical signals in an awake, conscious state to those in an unconscious state via anesthesia—which dampens communication between neurons so people feel no pain or become unconscious.

“What’s nice about anesthesia is that we have a reliable way of turning off consciousness,” says Eisen.

“So we’re now able to ask: What’s the fluctuation of electrical activity in a conscious versus unconscious brain? By characterizing how these states vary—with the precision enabled by computational models—we can start to build a better intuition for what underlies consciousness.”

Theories of consciousness

How are scientists thinking about consciousness? Eisen says that there are four major theories circulating in the neuroscience sphere. These theories are outlined below.

Global workspace theory

Consider the placement of your tongue in your mouth. This sensory information is always there, but you only notice the sensation when you make the effort to think about it. How does this happen?

“Global workspace theory seeks to explain how information becomes available to our consciousness,” he says. “This is called access consciousness—the kind that stores information in your mind and makes it available for verbal report. In this view, sensory information is broadcasted to higher-level regions of the brain by a process called ignition.” The theory proposes that widespread jolts of neuronal activity or “spiking” are essential for ignition, like how a few claps can lead to an audience applause. It’s through ignition that we reach consciousness.

Eisen’s research in anesthesia suggests, though, that not just any spiking will do. There needs to be a balance: enough activity to spark ignition, but also enough stability such that the brain doesn’t lose its ability to respond to inputs and produce reliable computations to reach consciousness.

Higher order theories

Let’s say you’re listening to “Here Comes The Sun” by The Beatles. Your brain processes the medley of auditory stimuli; you hear the bouncy guitar, upbeat drums, and George Harrison’s perky vocals. You’re having a musical experience—what it’s like to listen to music. According to higher-order theories, such an experience unlocks consciousness.

“Higher-order theories posit that a conscious mental state involves having higher-order mental representations of stimuli—usually in the higher levels of the brain responsible for cognition—to experience the world,” Eisen says.

Integrated information theory

“Imagine jumping into a lake on a warm summer day. All components of that experience—the feeling of the sun on your skin and the coolness of the water as you submerge—come together to form your ‘phenomenal consciousness,’” Eisen says. If the day was slightly less sunny or the water a fraction warmer, he explains, the experience would be different.

“Integrated information theory suggests that phenomenal consciousness involves an experience that is irreducible, meaning that none of the components of that experience can be separated or altered without changing the experience itself,” he says.

Attention schema theory

Attention schema theory, Eisen explains, says ‘attention’ is the information that we are focused on in the world, while ‘awareness’ is the model we have of our attention. He cites an interesting psychology study to disentangle attention and awareness.

In the study, the researchers showed human subjects a mixed sequence of two numbers and six letters on a computer. The participants were asked to report back what the numbers were. While they were doing this task, faintly detectable dots moved across the screen in the background. The interesting part, Eisen notes, is that people weren’t aware of the dots—that is, they didn’t report that they saw them. But despite saying they didn’t see the dots, people performed worse on the task when the dots were present.

“This suggests that some of the subjects’ attention was allocated towards the dots, limiting their available attention for the actual task,” he says. “In this case, people’s awareness didn’t track their attention. The subjects were not aware of the dots, even though the study shows that the dots did indeed affect their attention.”

The science behind consciousness

Eisen notes that a solid understanding of the neural basis of consciousness has yet to be cemented. However, he and his research team are advancing in this quest. “In our work, we found that brain activity is more ‘unstable’ under anesthesia, meaning that it lacks the ability to recover from disturbances—like distractions or random fluctuations in activity—and regain a normal state,” he says.

He and his fellow researchers believe this is because the unconscious brain can’t reliably engage in computations like the conscious brain does, and sensory information gets lost in the noise. This crucial finding points to how the brain’s stability may be a cornerstone of consciousness.

There’s still more work to do, Eisen says. But eventually, he hopes that this research can help crack the enduring mystery of how consciousness shapes human existence. “There is so much complexity and depth to human experience, emotion, and thought. Through rigorous research, we may one day reveal the machinery that gives us our common humanity.”

A new computational technique could make it easier to engineer useful proteins

Anne Trafton |

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

Woman gestures with her hand in front of a glass wall with equations written on it.
MIT Professor of Brain and Cognitive Sciences Ila Fiete in her lab at the McGovern Institute. Photo: Steph Stevens

“Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It’s like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University.

Optimizing proteins

Many naturally occurring proteins have functions that could make them useful for research or medical applications, but they need a little extra engineering to optimize them. In this study, the researchers were originally interested in developing proteins that could be used in living cells as voltage indicators. These proteins, produced by some bacteria and algae, emit fluorescent light when an electric potential is detected. If engineered for use in mammalian cells, such proteins could allow researchers to measure neuron activity without using electrodes.

While decades of research have gone into engineering these proteins to produce a stronger fluorescent signal, on a faster timescale, they haven’t become effective enough for widespread use. Bracha, who works in Edward Boyden’s lab at the McGovern Institute, reached out to Fiete’s lab to see if they could work together on a computational approach that might help speed up the process of optimizing the proteins.

“This work exemplifies the human serendipity that characterizes so much science discovery,” Fiete says.

“This work grew out of the Yang Tan Collective retreat, a scientific meeting of researchers from multiple centers at MIT with distinct missions unified by the shared support of K. Lisa Yang. We learned that some of our interests and tools in modeling how brains learn and optimize could be applied in the totally different domain of protein design, as being practiced in the Boyden lab.”

For any given protein that researchers might want to optimize, there is a nearly infinite number of possible sequences that could generated by swapping in different amino acids at each point within the sequence. With so many possible variants, it is impossible to test all of them experimentally, so researchers have turned to computational modeling to try to predict which ones will work best.

In this study, the researchers set out to overcome those challenges, using data from GFP to develop and test a computational model that could predict better versions of the protein.

They began by training a type of model known as a convolutional neural network (CNN) on experimental data consisting of GFP sequences and their brightness — the feature that they wanted to optimize.

The model was able to create a “fitness landscape” — a three-dimensional map that depicts the fitness of a given protein and how much it differs from the original sequence — based on a relatively small amount of experimental data (from about 1,000 variants of GFP).

These landscapes contain peaks that represent fitter proteins and valleys that represent less fit proteins. Predicting the path that a protein needs to follow to reach the peaks of fitness can be difficult, because often a protein will need to undergo a mutation that makes it less fit before it reaches a nearby peak of higher fitness. To overcome this problem, the researchers used an existing computational technique to “smooth” the fitness landscape.

Once these small bumps in the landscape were smoothed, the researchers retrained the CNN model and found that it was able to reach greater fitness peaks more easily. The model was able to predict optimized GFP sequences that had as many as seven different amino acids from the protein sequence they started with, and the best of these proteins were estimated to be about 2.5 times fitter than the original.

“Once we have this landscape that represents what the model thinks is nearby, we smooth it out and then we retrain the model on the smoother version of the landscape,” Kirjner says. “Now there is a smooth path from your starting point to the top, which the model is now able to reach by iteratively making small improvements. The same is often impossible for unsmoothed landscapes.”

Proof-of-concept

The researchers also showed that this approach worked well in identifying new sequences for the viral capsid of adeno-associated virus (AAV), a viral vector that is commonly used to deliver DNA. In that case, they optimized the capsid for its ability to package a DNA payload.

“We used GFP and AAV as a proof-of-concept to show that this is a method that works on data sets that are very well-characterized, and because of that, it should be applicable to other protein engineering problems,” Bracha says.

The researchers now plan to use this computational technique on data that Bracha has been generating on voltage indicator proteins.

“Dozens of labs having been working on that for two decades, and still there isn’t anything better,” she says. “The hope is that now with generation of a smaller data set, we could train a model in silico and make predictions that could be better than the past two decades of manual testing.”

The research was funded, in part, by the U.S. National Science Foundation, the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program, the DARPA Accelerated Molecular Discovery program, the Sanofi Computational Antibody Design grant, the U.S. Office of Naval Research, the Howard Hughes Medical Institute, the National Institutes of Health, the K. Lisa Yang ICoN Center, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

School of Science announces 2024 Infinite Expansion Awards

Anne Trafton |

The MIT School of Science has announced nine postdocs and research scientists as recipients of the 2024 Infinite Expansion Award, which highlights extraordinary members of the MIT community.

The following are the 2024 School of Science Infinite Expansion winners:

  • Sarthak Chandra, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Ila Fiete, who wrote, “He has expanded the research abilities of my group by being a versatile and brilliant scientist, by drawing connections with a different area that he was an expert in from his PhD training, and by being a highly involved and caring mentor.”
  • Michal Fux, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Pawan Sinha, who wrote, “She is one of those figurative beams of light that not only brilliantly illuminate scientific questions, but also enliven a research team.”
  • Andrew Savinov, a postdoc in the Department of Biology, was nominated by Associate Professor Gene-Wei Li, who wrote, “Andrew is an extraordinarily creative and accomplished biophysicist, as well as an outstanding contributor to the broader MIT community.”
  • Ho Fung Cheng, a postdoc in the Department of Chemistry, was nominated by Professor Jeremiah Johnson, who wrote, “His impact on research and our departmental community during his time at MIT has been outstanding, and I believe that he will be a worldclass teacher and research group leader in his independent career next year.”
  • Gabi Wenzel, a postdoc in the Department of Chemistry, was nominated by Assistant Professor Brett McGuire, who wrote, “In the one year since Gabi joined our team, she has become an indispensable leader, demonstrating exceptional skill, innovation, and dedication in our challenging research environment.”
  • Yu-An Zhang, a postdoc in the Department of Chemistry, was nominated by Professor Alison Wendlandt, who wrote, “He is a creative, deep-thinking scientist and a superb organic chemist. But above all, he is an off-scale mentor and a cherished coworker.”
  • Wouter Van de Pontseele, a senior postdoc in the Laboratory for Nuclear Science, was nominated by Professor Joseph Formaggio, who wrote, “He is a talented scientist with an intense creativity, scholarship, and student mentorship record. In the time he has been with my group, he has led multiple facets of my experimental program and has been a wonderful citizen of the MIT community.”
  • Alexander Shvonski, a lecturer in the Department of Physics, was nominated by Assistant Professor Andrew Vanderburg, who wrote, “… I have been blown away by Alex’s knowledge of education research and best practices, his skills as a teacher and course content designer, and I have been extremely grateful for his assistance.”
  • David Stoppel, a research scientist in The Picower Institute for Learning and Memory, was nominated by Professor Mark Bear and his research group, who wrote, “As impressive as his research achievements might be, David’s most genuine qualification for this award is his incredible commitment to mentorship and the dissemination of knowledge.”

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.

A mindful McGovern community

by Julie Pryor |

Mindfulness is the practice of maintaining a state of complete awareness of one’s thoughts, emotions, or experiences on a moment-to-moment basis. McGovern researchers have shown that practicing mindfulness reduces anxiety and supports emotional resilience.

In a survey distributed to the McGovern Institute community, 57% of the 74 researchers, faculty, and staff who responded, said that they practice mindfulness as a way to reduce anxiety and stress.

Here are a few of their stories.

Fernanda De La Torre

Portrait of a smiling woman leaning back against a railing.
MIT graduate student Fernanda De La Torre. Photo: Steph Stevens

Fernanda De La Torre is a graduate student in MIT’s Department of Brain and Cognitive Sciences, where she is advised by Josh McDermott.

Originally from Mexico, De La Torre took an unconventional path to her education in the United States, where she completed her undergraduate studies in computer science and math at Kansas State University. In 2019, she came to MIT as a postbaccalaureate student in the lab of Tomaso Poggio where she began working on deep-learning theory, an area of machine learning focused on how artificial neural networks modeled on the brain can learn to recognize patterns and learn.

A recent recipient of the prestigious Paul and Daisy Soros Fellowship for New Americans, De La Torre now studies multisensory integration during speech perception using deep learning models in Josh McDermott’s lab.

What kind of mindfulness do you practice, how often, and why?

Metta meditation is the type of meditation I come back to the most. I practice 2-3 times per week. Sometimes by joining Nikki Mirghafori’s Zoom calls or listening to her and other teachers’ recordings on AudioDharma. I practice because when I observe the patterns of my thoughts, I remember the importance of compassion, including self-compassion. In my experience, I find metta meditation is a wonderful way to cultivate the two: observation and compassion. 

When and why did you start practicing mindfulness?

My first meditation practice was as a first-year post-baccalaureate student here at BCS. Gal Raz (also pictured above) carried a lot of peace and attributed it to meditation; this sparked my curiosity. I started practicing more frequently last summer, after realizing my mental health was not in a good place.

How does mindfulness benefit your research at MIT?

This is hard to answer because I think the benefits of meditation are hard to measure. I find that meditation helps me stay centered and healthy, which can indirectly help the research I do. More directly, some of my initial grad school pursuits were fueled by thoughts during meditation but I ended up feeling that a lot of these concepts are hard to explore using non-philosophical approaches. So I think meditation is mainly a practice that helps my health, my relationships with others, and my relationship with work (this last one I find most challenging and personally unresolved). 

Adam Eisen

MIT graduate student Adam Eisen.

Adam Eisen is a graduate student in MIT’s Department of Brain and Cognitive Sciences, where he is co-advised by Ila Fiete (McGovern Institute) and Earl Miller (Picower Institute).

Eisen completed his undergraduate degree in Applied Mathematics & Computer Engineering at Queen’s University in Toronto, Canada. Prior to joining MIT, Eisen built computer vision algorithms at the solar aerial inspection company Heliolytics and worked on developing machine learning tools to predict disease outcomes from genetics at The Hospital for Sick Children.

Today, in the Fiete and Miller labs, Eisen develops tools for analyzing the flow of neural activity, and applies them to understand changes in neural states (such as from consciousness to anesthetic-induced unconsciousness).

What kind of mindfulness do you practice, how often, and why?

I mostly practice simple sitting meditation centered on awareness of senses and breathing. On a good week, I meditate about 3-5 times. The reason I practice are the benefits to my general experience of living. Whenever I’m in a prolonged period of consistent meditation, I’m shocked by how much more awareness I have about thoughts, feelings and sensations that are arising in my mind throughout the day. I’m also amazed by how much easier it is to watch my mind and body react to the context around me, without slipping into the usual patterns and habits. I also find mindful benefits in doing yoga, running and playing music, but the core is really centered on meditation practice.

When and why did you start practicing mindfulness?

I’ve been interested in mindfulness and meditation since undergrad as a path to investigating the nature of mind and thought – an interest which also led me into my PhD. I started practicing meditation more seriously at the start of the pandemic to get more first hand experience with what I had been learning about. I find meditation is one of those things where knowledge and theory can support the practice, but without the experiential component it’s very hard to really start to build an understanding of the core concepts at play.

How does mindfulness benefit your research at MIT?

Mindfulness has definitely informed the kinds of things I’m interested in studying and the questions I’d like to ask – largely in relation to the nature of conscious awareness and the flow of thoughts. Outside of that, I’d like to think that mindfulness benefits my general well-being and spiritual balance, which enables me to do better research.

 

Sugandha Sharma

Woman clasping hands in a yoga pose, looking directly into the camera.
MIT graduate student Sugandha Sharma. Photo: Steph Stevens

Sugandha (Su) Sharma is a graduate student in MIT’s Department of Brain and Cognitive Sciences (BCS), where she is co-advised by Ila Fiete (McGovern Institute) and Josh Tenenbaum (BCS).

Prior to joining MIT, she studied theoretical neuroscience at the University of Waterloo where she built neural models of context dependent decision making in the prefrontal cortex and spiking neuron models of bayesian inference, based on online learning of priors from life experience.

Today, in the Fiete and Tenenbaum labs, she studies the computational and theoretical principles underlying cognition and intelligence in the human brain.  She is currently exploring the coding principles in the hippocampal circuits implicated in spatial navigation, and their role in cognitive computations like structure learning and relational reasoning.

When did you start practicing mindfulness?

When I first learned to meditate, I was challenged to practice it every day for at least 3 months in a row. I took up the challenge, and by the end of it, the results were profound. My whole perspective towards life changed. It made me more empathetic — I could step in other people’s shoes and be mindful of their situations and feelings;  my focus shifted from myself to the big picture — it made me realize how insignificant my life was on the grand scale of the universe, and how it was worthless to be caught up in small things that I was usually worrying about. It somehow also brought selflessness to me. This experience hooked me to meditation and mindfulness for life!

What kind of mindfulness do you practice and why?

I practice mindfulness because it brings awareness. It helps me to be aware of myself, my thoughts, my actions, and my surroundings at each moment in my life, thus helping me stay in and enjoy the present moment. Awareness is of utmost importance since an aware mind always does the right thing. Imagine that you are angry, in that moment you have lost awareness of yourself. The moment you become aware of yourself; anger goes away. This is why sometimes counting helps to combat anger. If you start counting, that gives you time to think and become aware of yourself and your actions.

Meditating — sitting with my eyes closed and just observing (being aware of) my thoughts — is a yogic technique that helps me clear the noise in my mind and calm it down making it easier for me to be mindful not only while meditating, but also in general after I am done meditating. Over time, the thoughts vanish, and the mind becomes blank (noiseless). For this reason, practicing meditation regularly makes it easier for me to be mindful all the time.

An added advantage of yoga and meditation is that it helps combat stress by relaxing the mind and body. Many people don’t know what to do when they are stressed, but I am grateful to have this toolkit of yoga and meditation to deal with stressful situations in my life. They help me calm my mind in stressful situations and ensure that instead of reacting to a situation, I instead act mindfully and appropriately to make it right.