Algorithms and circuits for motor control and learning in the songbird

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Stetner, Michael E.(Michael Edward)

Advisor dc:contributor.advisor

• Michale S. Fee.

• Brain and Cognitive Sciences.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121829

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121829