Structure and Time Course of Neural Population Activity During Learning

Our ability to perform a variety of difficult tasks everything from reasoning about the best chess move, to shooting a free throw, or finely dicing an onion is due to the coordinated activity of populations of neurons throughout the nervous

system. And yet, we lack an understanding of how the brain generates the activity appropriate for achieving something as simple as pressing an elevator button. In part, this is because we do not know which neural activity patterns the brain is capable of generating, nor how that activity will change with experience. By exploring the structure and constraints on the activity patterns the brain can express, we move closer to understanding how the brain can generate the activity supportive of such a rich variety of behaviors and adaptations. Presently, in studies of arm or eye movements, we typically don't know the causal relationship between neural activity and behavior. Here we use a brain-computer interface (BCI) paradigm to study learning, because the exact relationship between neural activity and behavior is controlled by the experimenter. To generate proficient behavior, the animal must change the activity of the neurons currently being recorded. This provides us with the means to causally relate any observed structure in neural population activity with animals' performance at the task. The focus of this thesis is to characterize the structure and time course of neural population activity during learning. In the first part of this thesis, we note that just as there is more than one way to win a game of chess, the brain has many different

patterns of neural activity it can produce to drive the same behavior. Which of these redundant options does the brain prefer? We find that the frequency with which animals used different patterns of neural population activity was remarkably similar before and after learning. This suggests that the brain's ability to take advantage of redundancy may be somewhat limited, at least within the span of a few hours. In the second part of this thesis, we asked how internal states such as our arousal, attention, and motivation interact with how we learn new tasks. We identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term
eural engagement." We find that stereotyped changes in neural engagement during learning were unrelated to goal-seeking behavior, but nevertheless influenced how quickly different tasks were learned. Overall, this thesis characterizes a variety of different constraints and influences on how populations of neurons change their activity during learning.