Precise control of the tongue is necessary for drinking, eating, and vocalizing. Yet because tongue movements are fast and difficult to resolve, neural control of lingual kinematics remains poorly understood. We combine kilohertz frame-rate imaging and a deep-learning based artificial neural network to resolve 3D tongue kinematics in mice performing a cued lick task. Cue-evoked licks exhibit previously unobserved fine-scale movements which, like a hand searching for an unseen object, were produced after misses and were directionally biased towards remembered locations. Photoinhibition of anterolateral motor cortex (ALM) abolished these fine-scale adjustments, resulting in well-aimed but hypometric licks that missed the spout. Our results show that cortical activity is required for online corrections during licking and reveal novel, limb-like dynamics of the mouse tongue as it reaches for, and misses, targets. Time permitting, I will also share our recent discovery that male songbirds turn off their dopamine-based self-evaluation system when they perform for females. 


Jesse received his B.S. from Haverford College and his MD/PhD degrees from Columbia University. His PhD with Rafa Yuste focused on dendritic computation and microcircuits of the cerebral cortex. In medical school, he became interested disorders such as Parkinson's and dystonia that degrade basal ganglia dependent reinforcement learning. His postdoctoral work at MIT focused on how the basal ganglia implement RL in juvenile songbirds. His lab in the Department of Neurobiology and Behavior at Cornell University, founded in 2013, is interested in how animals learn skills through practice. The Goldberg lab studies vocal control in songbirds and parrots and motor control by comparing neural mechanisms by which mice aim their limbs and tongues. For all research programs, the lab combines high channel count awake-behaving electrophysiology, closed-loop optogenetics, and machine learning-guided behavioral analysis. Jesse has been supported by the Pew, Klingenstein, Kavli, NIH New Innovator and Cornell Neurotech. His guiding philosophy is that comparative approaches (across species and across the animal-machine divide) are necessary to distinguish general principles from behavior-, effector-, machine- and species-specific solutions to motor learning problems. 

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