How is Performance Evaluation Encoded in the Brain?

点击翻译按钮获取中文摘要

Dopamine (DA)-basal ganglia (BG) circuits are critical for motor control and learning. Our current understanding of these circuits comes largely from studies of animals learning for external rewards such as food or juice. Yet symptoms of diseases such as Parkinson’s, Huntington’s and dystonia include degradation of motor behaviors unrelated to reward seeking. In fact most of our behaviors, such as learning a sport or an instrument, are not simple actions in pursuit of rewards but are instead complex motor sequences learned by matching performance to internal goals. Mechanisms of this type of motor learning are poorly understood. The songbird model offers a unique opportunity to study how internally guided motor sequences are constructed. First, birdsong is learned by matching a complex vocal sequence to the memory of a tutor song, or ‘template’. Second, song learning requires a DA-BG circuit that is part of a tractable ‘song system.’ We will leverage these advantages to decipher how motor sequences are learned during ‘natural’ trial and error. To test if DA encodes error during internally-guided performance evaluation, I will record BG-projecting VTA neurons as I induce auditory error in specific song syllables using distorted auditory feedback (Aim 1, K99 phase). Preliminary results suggest DA encodes performance error, the difference between actual and predicted performance. DA activity was phasically suppressed after distorted syllables, consistent with a worse-than- predicted outcome, and was phasically activated at the precise moment of the song when a predicted distortion did not occur, consistent with a better-than-predicted outcome. Next I will resolve the paradox (Aim 2, K99 phase) of how DA activity both evaluates past behavior for learning and also modulates ongoing motor variability by recoding BG-projecting VTA neurons as birds transition from singing alone (variable ‘practice mode’) to singing to a female (stereotyped ‘performance mode’). Finally I will develop optical techniques (Aim 3.1) to chronically monitor VTA neurons over learning-relevant timescales to determine the origins and consequences of DA performance error (Aims 3.2 and 3.3, R00 phase). My mentor, Dr. Jesse Goldberg, co-mentor Dr. Joseph Fetcho, and collaborators Drs. Melissa Warden, Chris Schaffer, and Nozomi Nishimura all have extensive expertise in calcium imaging and optogenetics. Developing these techniques, along with frequent data presentations, attendance of seminars and professional courses, and close interactions with the strong collaborative Cornell neuroscience community, will equip me with the necessary skills for transitioning to independence. In the independent R00 phase, I will use these acquired skills and innovative behavioral, optical, and computational techniques to complete the proposed aims (Aims 3.2 and 3.3) and establish an independent research program focused on the neural mechanisms of natural motor sequence learning.