During my internship at Erasmus MC in the Neuroscience Department, I focused on decoding electrophysiological data from deep cerebral nuclei and the medial prefrontal cortex (mPFC) to explore neural mechanisms underlying behavior and motor control. A significant portion of my work involved using advanced machine learning techniques to analyze the data obtained from these regions of the brain, ultimately contributing to our understanding of how the brain coordinates complex behaviors.
I developed and implemented data analysis pipelines, which allowed for better visualization and interpretation of neural activity. Specifically, I conducted Principal Component Analysis (PCA) on mPFC neurons during eyeblink conditioning tasks, aiming to identify and characterize the dynamics of neural populations during behavioral learning. Additionally, I worked on improving that pipeline with Demixed Principal Component Analysis (DPCA) to assess neuronal activity in the deep cerebellum and mPFC during a time-differentiating animal model experiment, which was designed to enhance our ability to interpret neural responses.
