The geometry of neural population activity during motor learning and memory

<p> The human brain is a marvel of complexity, with billions of neurons and trillions<br> of connections that allow us to perform an astounding array of behaviors, from basic<br> movements like walking and grasping to complex cognitive processes like decision-<br> making and language. However, despite decades of research, much about how the<br> brain learns and remembers remains a mystery.</p> <p><br> One challenge in understanding the neural basis of learning and memory is that<br> seemingly simple acts, like taking a sip of water, are in fact immensely complex<br> processes that require an intricate coordination of neural activity. Yet humans are<br> able to learn and remember how to perform a vast array of new skills, suggesting<br> that the brain has the capacity to produce neural activity appropriate for a wide<br> variety of tasks.</p> <p><br> To better understand how the brain learns and remembers new behaviors, it is<br> important to investigate the interplay between learning and memory. However, this<br> relationship is not well understood, and a better understanding of it could shed light<br> on how the brain is able to learn new tasks without forgetting familiar ones.</p> <p><br> A difficulty in probing how the brain learns and remembers different tasks is that<br> the relationship between neural activity and behavior is very complex and difficult to<br> estimate. To circumvent this obstacle, we employ a brain-computer interface (BCI).<br> A BCI allows the experimenters to specify the causal relationship between neural<br> activity and behavior, which can provide insights into the underlying mechanisms of<br> learning and memory. This is in contrast to traditional arm-reaching experiments,<br> where the relationship between neural activity and behavior is largely unknown.</p> <p><br> The focus of this thesis is to explore the geometry of neural population activity<br> during motor learning and memory. By leveraging the causal relationship between<br> neural activity and behavior, we aim to shed light on the complex processes that<br> underlie motor memories, and to provide insights into how the brain is able to learn<br> and retain new behaviors. The pinnacle result of this thesis is that learning alters<br> neural activity to simultaneously support both memory and action - i.e. learning<br> leaves a “memory trace” in neural population activity.<br> </p>