Analytical Comparison of Slow Feature Analysis and the Successor Representation

Slow feature analysis (SFA) and the successor representation (SR) share important mathematical properties and are both relevant to the study of spatial representations in neuroscience. This work explores the connection between these two methods, showing that various SFA algorithms can be formulated as eigenvalue problems involving the SR and related quantities.