This course provides an in-depth survey and understanding of modern computational approaches to design and analyses of neuroimaging data. The course is a mixture of lectures and projects geared to give the student an understanding of the possibilities as well as limitations of different computational approaches. Topics include: signal and noise in MRI; general linear modeling; fMRI-adaptation; multivoxel pattern analyses; decoding and encoding algorithms; modeling spatiotemporal population receptive fields; using deep neural networks to model brain activations. Required: Instructor Consent; Recommended: Cognitive Neuroscience. Linear Algebra
3 units · Letter (ABCD/NP)
This course provides an in-depth survey and understanding of modern computational approaches to design and analyses of neuroimaging data. The course is a mixture of lectures and projects geared to give the student an understanding of the possibilities as well as limitations of different computational approaches. Topics include: signal and noise in MRI; general linear modeling; fMRI-adaptation; multivoxel pattern analyses; decoding and encoding algorithms; modeling spatiotemporal population receptive fields; using deep neural networks to model brain activations. Required: Instructor Consent; Recommended: Cognitive Neuroscience. Linear Algebra
Offered in Spring 2027 at Stanford University.