January 3, 2023

Course description:
Brains are remarkably complex, massive networks of interconnected neurons that underlie our abilities to intelligently sense, reason, learn, and interact with our world. Technologies for monitoring neural activity in the brain are revealing rich structure within the coordinated activity of these interconnected populations of neurons. In this course, we will discuss deep learning models that can be applied toward 1) understanding how neural activity in the brain gives rise to intelligent behavior and 2) designing algorithms for brain-interfacing biomedical devices. Topics will focus around variational autoencoders and recurrent neural networks, along with their probabilistic foundations from classical machine learning. Coursework will include readings from the deep learning and computational neuroscience literature, programming assignments, and a final modeling project applied to neural population data.

Multivariate calculus, probability & statistics, linear algebra, and some exposure to machine learning. Programming assignments will be completed in Python. No prior knowledge of neuroscience is needed.

Syllabus and additional info available here: