Algorithms and circuits for motor control and learning in the songbird

Abstract

From riding a bike to brushing our teeth, we learn many of our motor skills through trial and error. Many biologically based trial and error learning models depend on a teaching signal from dopamine neurons. Dopamine neurons increase their firing rates to signal outcomes that are better than expected and decrease their firing rates to signal outcomes that are worse than expected. This dopamine signal is thought to control learning by triggering synaptic changes in the basal ganglia. What are the origins of this dopaminergic teaching signal? How do synaptic changes in the basal ganglia lead to changes in behavior? In this thesis, I study these questions in a model of skill learning - the songbird. In the first part of my thesis, I develop a computational model of song learning. This model incorporates a dopaminergic reinforcement signal in VTA and dopamine-dependent synaptic plasticity in the singing-related part of the basal ganglia.

I demonstrate that this model can provide explanations for a variety of experimental results from the literature. In the second part of my thesis, I investigate a potential source of the dopaminergic error signal in VTA. I performed the first recordings from one cortical input to VTA: the dorsal intermediate arcopallium (AId). Previous studies disagree on the role of Ald in behavior. Some studies argue that AId contributes vocal error information to VTA. Other studies suggest that AId is not involved in the computation of error signals, but is instead responsible for controlling head and body movements. I directly tested these hypotheses by recording single neurons in AId during singing and during natural movements. My results support a motor role for AId - AId neurons had highly significant changes in activity during head and body movements. Meanwhile, following vocal errors Aid neurons had small but marginally significant decrease in firing rate.

In a more detailed analysis, I developed an automated behavior classification algorithm to categorize zebra finch behavior and related these behavior classes to the activity of single units in Aid. My results support the hypothesis that AId is part of a general-purpose motor control network in the songbird brain.