Using Machine Learning to Analyze Brain Activity During a Short-Term Memory Task
Name
Kristina Martšenko
Abstract
We analyze the electrical activity of neurons in the prefrontal cortex of a monkey while it performs a task requiring short-term memory. In the first part of the analysis we use supervised machine learning to see if we can predict the monkey's behavior from its brain activity. We find that, while unable to predict the behavior before it occurs, we are able to correctly determine it based on post-behavior brain activity 69% of the time. In the second part of the analysis we investigate how the activity of neurons changes during a day of repeating the task hundreds of times. We find that for many neurons it remains the same, but for some it increases or decreases. In addition, we find that how the activity of a neuron changes over the day is not related to how the neuron behaves during the task. These findings can lead to a better understanding of the properties of the prefrontal cortex and short-term memory.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Raul Vicente, Kristjan Korjus
Defence year
2014