Shreya Saxena, an Indian American assistant professor of biomedical engineering at Yale Engineering, is trying to figure out the impossibly mysterious and complex ways the brain works to help us perform everyday tasks.
Grabbing your keys off the counter and putting them in your pocket seems like a simple task that we often take for granted, according to a story published in Yale Engineering. But try getting a robot or prosthetic device to do so with the same fluidity, and it becomes clear how remarkably complex the motions are, as well as the brain that makes them possible.
For a long time, the brain seemed like such an impossibly mysterious and ineffable thing. In many ways, it still does, but technology is slowly peeling away the layers that shroud it.
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For instance, Saxena noted, it is becoming clear that computations in the brain are a function of groups of neurons and non-neuronal cells working together. Nonetheless, she says, there’s still a lot more to uncover.
Saxena’s interest in the human brain was sparked as an undergraduate by a lecture on how computational modeling can help decipher the brain.
“I was inspired by just how good the brain is at achieving things, and how little we know about it,” she said, adding that she wants to understand how we can “learn from the best machine in the world.”
To that end, her many projects include building computational models of monkey’s forelimbs, working with a dance troupe to mine more insights about the brain’s role in movement, and simulating how animals cooperate with each other for rewards.
“The machines we build — we know how they work partly because we’ve built them,” she said. “With the brain, we’re not there yet. Part of what my lab is focusing on is reverse-engineering the brain. If we know how to build it, or if we can predict the outcomes during different tasks, then we should be able to understand all about it. But it’s a tall order, so we are a very large number of people across the world who are actually working on this.”
Saxena’s lab is focused on research at the intersection of neuroscience and artificial intelligence (AI). One emphasis of her work is control theory, which involves how all the many components of the brain work together to achieve a desired result through our bodies.
She spends much of her time creating computer models of the brain’s workings, with applications in sensorimotor control (how your brain combines motor commands with sensory information to get your body to move), decision- making, and social behavior.
She applies AI to neuroscience in a couple of ways. One she refers to as a data-driven approach, in which she uses AI to directly model data recorded from the brain. For the other, a goal-driven approach, AI acts like the brain itself, emulating its functions and computations in specific contexts.
Each of these approaches is made possible by advances in hardware technology. We can now record activity from thousands of single neurons in different cell types, as well as the activity of groups of neurons in many regions of the brain, while simultaneously recording high-resolution behavioral videos.
The incredible complexity and sheer amount of this data requires the kinds of advances made in AI to make sense of it. Also, advances in computing technology have led to more efficient algorithms and increased computational resources, better allowing AI to emulate brain function.
This can help researchers understand much more about how the brain works by providing digital twins of different brain regions — in turn, this knowledge can help build better AI.
Really complex movements — like those of ballet dancers — also fascinate Saxena. What’s happening in the brain to coordinate all those motions is a tangle of questions that still baffles researchers. Saxena began tackling some of those questions last year as part of a collaboration with the Dance Theater of Harlem.
Saxena and Samuel McDougle, assistant professor of psychology, worked with the dance troupe in a presentation on dance and human cognition.
“It was a conversation between the dancers, the choreographer, the researchers, and the audience,” she said. The conversations were interspersed with the dancers performing demonstrations to illustrate motor control and the cognition of movements. “Some of these were well-learned movements, versus something you’re just learning on the fly, while we discussed the psychology and the neuroscience of all of these movements. It was amazing.”
So, what are the cognitive mechanisms when a group of dancers perform a pirouette or a grand jeté in sync? That’s the next mystery for Saxena’s lab to work on.