BME PhD Defense Announcement for Seungbin Park (Maria Dadarlat, advisor)

Everyone is invited to attend the public presentation beginning at 1:00 PM.

Title: Determining the neural encoding of sensorimotor function in the mouse cortex using machine learning

Date: March 11th Tuesday

Time: 1:00 PM

Location: MJIS 2001

Committee:

    Dr. Maria C. Dadarlat, Chair, Weldon School of Biomedical Engineering

    Dr. Eugenio Culurciello, Weldon School of Biomedical Engineering

    Dr. Young Kim, Weldon School of Biomedical Engineering

    Dr. Joseph Makin, Elmore Family School of Electrical and Computer Engineering

Abstract: Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. Elucidating the neural encoding of sensorimotor function in the cortex and decoding sensory and motor commands from these brain areas is essential for the advancement of BMIs. In this defense, I mainly present two studies for determining the neural encoding of sensorimotor function in the mouse cortex using machine learning on neural data recorded with two-photon (2p) calcium imaging. The first study applies deep learning to decode multi-limb movements of running mice from 2p calcium imaging data, demonstrating that an artificial neural network can accurately infer movements of all four limbs from neural activity in a single cortical hemisphere. The second study investigates how somatosensation is encoded at the population level in the mouse sensorimotor cortex, using principal component analysis on three 2p calcium imaging datasets from anesthetized and awake mice with passive or spontaneous limb movements. Together, these studies leverage 2p calcium imaging and machine learning to enhance our understanding of how sensorimotor information is encoded in the mouse cortex, ultimately contributing to the foundational knowledge required to develop advanced BMIs.