Seminar · Computational Neuroscience
Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies
James Haxby · Dartmouth College
Fri, Jun 26, 2020 · 14:30 UTC
Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment jointly models shared information and idiosyncratic topographies. Pattern vectors for neural responses and connectivities are projected into a common, high-dimensional information space, rather than being aligned in a canonical anatomical space. Hyperalignment calculates individual transformation matrices that preserve the geometry of pairwise dissimilarities between pattern vectors. Individual cortical topographies are modeled as mixtures of overlapping, individual-specific