Finding the brain’s compass
Undaunted by complex neural datasets, Ila Fiete’s lab is uncovering how we interact with the world
The brain is constantly making complicated calculations, reasoning about our place in the world. Mining vast volumes of neural data to uncover how neural dynamics and coding interact and underlie these calculations is daunting, but it promises fascinating insight into the workings of the brain.
Ila Fiete and her lab relish this challenge and are adding to the growing range of theoretical tools available for it. Broadly speaking, they build theoretical models and tools that unpack the brain’s computations, even as it interacts with the world. Fiete’s focus includes describing how neural plasticity shapes networks to perform computations, and a key goal is to understand how the brain represents and manipulates this information. The field of spatial navigation by the brain is currently providing a trove of data that likely contains insight into these computations. By forging new approaches to analyzing complex datasets, Fiete, an associate professor in MIT’s Department of Brain and Cognitive Sciences, and colleagues are helping to access the circuits underlying a range of phenomena including short term memory and reasoning. Most recently, they have made strides in uncovering how the brain stably tracks the direction the head is facing.
This is a seemingly simple problem for the brain to solve, but the world is constantly bombarding our senses with information. How 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? The Fiete lab recently identified a brain circuit in mice that distills such “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 Fiete, who is also an associate member of the McGovern Institute for Brain Research. “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.
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 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 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 a 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.
“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. Moser recently shared the Nobel Prize for Medicine for discovering cell types linked to navigation and was not involved in Fiete’s 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. 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 as an attractor. If neurons stray off trajectory, they are drawn back to it, the system quickly correcting itself. The ring’s property of attraction 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 the coding of head direction.
“Similar organization is probably present for other cognitive functions. So, the [study] 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.”
The Fiete lab’s recent study demonstrates how complex neural data from systems neuroscience can be unlocked to reveal how the brain encodes head direction. This gives us a glimpse into how we view ourselves relative to the world at large, and this glimpse can be expanded to better understand how other attributes are encoded.
Ila Fiete is an associate professor in the Department of Brain and Cognitive Sciences and a member of the McGovern Institute for Brain Research at MIT. Her research focuses on brain circuitry, using computation and theory to understand how the brain computes and codes information with respect to the shape and plasticity of neural circuits, as well as errors in that code, to understand the dynamics of learning.
This article appeared in the Summer 2020 issue of Science@MIT