The quest to understand what’s happening inside the minds and brains of animals has taken neuroscientists down many surprising paths: from peering directly into living brains, to controlling neurons with bursts of light, to building intricate contraptions and virtual reality environments.
In 2013, it took the neurobiologist Bob Datta and his colleagues at Harvard Medical School to a Best Buy down the street from their lab.
At the electronics store, they found what they needed: an Xbox Kinect, a gaming device that senses a player’s motions. The scientists wanted to monitor in exhaustive detail the body movements of the mice they were studying, but none of the usual laboratory techniques seemed up to the task. So Datta’s group turned to the toy, using it to collect three-dimensional motor information from the animals as they explored their environment. The device essentially rendered them as clouds of points in space, and the team then analyzed the rhythmic movement of those points.
Datta’s solution might have been unorthodox at the time, but it’s now emblematic of a wave of automated approaches that are transforming the science of behavior. By studying animals’ behaviors more rigorously and quantitatively, researchers are hoping for deeper insights into the unobservable “drives,” or internal states, responsible for them. “We don’t know the possible states an animal can even be in,” wrote Adam Calhoun, a postdoctoral fellow who studies animal behavior at Princeton University.
Tracing those internal states back to specific activity in the brain’s complex neural circuitry presents a further hurdle. Although sophisticated tools can record from thousands of neurons at once, “we don’t understand the output of the brain,” Datta said. “Making sense of these dense neural codes is going to require access to a richer understanding of behavior.”
That richer understanding may not remain out of reach much longer. Capitalizing on advances in machine learning, scientists are building algorithms that automatically track animals’ movements, down to tiny changes in the angle of a fly’s wing or the arch of a mouse’s back. They’re also creating pattern-finding tools that automatically analyze and classify this data for clues about animals’ internal states.
A key advantage of these methods is that they can pick up on patterns that humans can’t see. In a paper published last month in Nature Neuroscience, Calhoun, with the Princeton neuroscientists Mala Murthy and Jonathan Pillow, built a machine learning model that used behavioral observations alone to identify three internal states underlying the courtship behavior of fruit flies. By manipulating the flies’ brain activity, the researchers were then able to pinpoint a set of neurons that controlled those states.
The work on motion tracking and behavioral analysis that made these findings possible represents a technological revolution in the study of behavior. It also indicates that this success is just one of many to come. Scientists are now applying these methods to tackle questions in neuroscience, genetics, evolution and medicine that seemed unsolvable until now.
Armed with pen, paper and stopwatch, scientists have been quantifying animal behavior in the wild (and in their labs) for decades, watching their subjects sleep and play and forage and mate. They’ve tallied observations and delineated patterns and come up with organizational frameworks to systematize and explain those trends. (The biologists Nikolaas Tinbergen, Konrad Lorenz and Karl von Frisch won a Nobel Prize in 1973 for independently performing these kinds of experiments with fish, birds and insects.)
The inventories of behaviors arising from this work could get extremely detailed: A description of a mouse’s grooming in a 1973 Nature article involved a “flurry of forelimbs below face” and “large synchronous but asymmetric strokes of forelimbs over top of head,” with estimates of how likely such gestures might be under different circumstances. Researchers needed to capture all that detail because they couldn’t know which aspects of the observed behaviors might turn out to be important.
Some scientists have taken the opposite tack, reducing animals’ behavioral variability to its bare bones by putting them in controlled laboratory settings and allowing them to make only simple binary decisions, like whether to turn left or right in a maze. Such simplifications have sometimes been useful and informative, but artificial restrictions also compromise researchers’ understanding of natural behaviors and can cause them to overlook important signals. “Having a good grasp on the behavior is really the limiting factor for this research,” said Ann Kennedy, a postdoctoral researcher in theoretical neuroscience at the California Institute of Technology.
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