Analyzing a complex motor act at the mesoscopic scale
- Awardees
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Michael A. Long, Ph.D. New York University
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Liam Paninski, Ph.D. Columbia University
Our motor systems are impressive. We can learn to effortlessly produce highly skilled muscle movements, such as hitting a golf ball or playing the violin. Our brains must step through sequential patterns of activity that enable these kinds of behaviors, but we know very little about how this is accomplished. To investigate this issue, our laboratory studies another skilled, complex motor behavior: the courtship song of the zebra finch. This song is learned from the bird’s father in a process that parallels speech acquisition in infants. Yet, unlike speech production, much is known about the neural circuits that produce singing behavior. The song is driven by a network of brain structures, one of which contains the circuitry for generating the sequence of syllables that compose the song. Different neurons in that region show a flurry of activity at different points in time during each song. Little is known about how the song is represented in these neurons, in part because we lack methods to record from multiple neurons simultaneously in this brain area. Using 2-photon microscopy, we have developed a method to watch the neurons in the brain of a zebra finch while they produce the song. Our lab will work in close collaboration with theoretical neuroscientist Liam Paninski to develop quantitative methods to analyze these data. Our results are already beginning to shed light onto how complex, learned motor acts are generated not just in birdsong, but also in other complex movements both in health and disease.