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    Home»Science»Your Brain Uses Calculus to Control Fast Movements
    Science

    Your Brain Uses Calculus to Control Fast Movements

    By AdminDecember 11, 2022
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    Your Brain Uses Calculus to Control Fast Movements


    A mouse is running on a treadmill embedded in a virtual reality corridor. In its mind’s eye, it sees itself scurrying down a tunnel with a distinctive pattern of lights ahead. Through training, the mouse has learned that if it stops at the lights and holds that position for 1.5 seconds, it will receive a reward—a small drink of water. Then it can rush to another set of lights to receive another reward.

    This setup is the basis for research published in July in Cell Reports by the neuroscientists Elie Adam, Taylor Johns and Mriganka Sur of the Massachusetts Institute of Technology. It explores a simple question: How does the brain—in mice, humans and other mammals—work quickly enough to stop us on a dime? The new work reveals that the brain is not wired to transmit a sharp “stop” command in the most direct or intuitive way. Instead, it employs a more complicated signaling system based on principles of calculus. This arrangement may sound overly complicated, but it’s a surprisingly clever way to control behaviors that need to be more precise than the commands from the brain can be.

    Control over the simple mechanics of walking or running is fairly easy to describe: The mesencephalic locomotor region (MLR) of the brain sends signals to neurons in the spinal cord, which send inhibitory or excitatory impulses to motor neurons governing muscles in the leg: Stop. Go. Stop. Go. Each signal is a spike of electrical activity generated by the sets of neurons firing.

    The story gets more complex, however, when goals are introduced, such as when a tennis player wants to run to an exact spot on the court or a thirsty mouse eyes a refreshing prize in the distance. Biologists have understood for a long time that goals take shape in the brain’s cerebral cortex. How does the brain translate a goal (stop running there so you get a reward) into a precisely timed signal that tells the MLR to hit the brakes?

    “Humans and mammals have extraordinary abilities when it comes to sensory motor control,” said Sridevi Sarma, a neuroscientist at Johns Hopkins University. “For decades people have been studying what it is about our brains that makes us so agile, quick and robust.”

    The Fast and the Furriest

    To understand the answer, the researchers monitored the neural activity in a mouse’s brain while timing how long it took the animal to decelerate from top speed to a full stop. They expected to see an inhibitory signal surge into the MLR, triggering the legs to stop almost instantaneously, like an electrical switch turning off a lightbulb.

    The neuroscientist Mriganka Sur and his colleagues found that in a mouse’s brain, a precise physical command was encoded in the interval between the spikes of two neural signals. “There is no information in the height of the spikes,” he said.

    Photograph: Webb Chappell 

    But a discrepancy in the data quickly undermined that theory. They observed a “stop” signal flowing into the MLR while the mouse slowed, but it wasn’t spiking in intensity fast enough to explain how quickly the animal halted.

    “If you just take stop signals and feed them into the MLR, the animal will stop, but the mathematics tell us that the stop won’t be fast enough,” said Adam.

    “The cortex doesn’t provide a switch,” said Sur. “We thought that’s what the cortex would do, go from 0 to 1 with a fast signal. It doesn’t do that, that’s the puzzle.”

    So the researchers knew there had to be an additional signaling system at work.

    To find it, they looked again at the anatomy of the mouse brain. Between the cortex where goals originate and the MLR that controls locomotion sits another region, the subthalamic nucleus (STN). It was already known that the STN connects to the MLR by two pathways: One sends excitatory signals and the other sends inhibitory signals. The researchers realized that the MLR responds to the interplay between the two signals rather than relying on the strength of either one.



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