A Low-Rank Method for Characterizing High-Level Neural Computations.

ABSTRACT: The signal transformations that take place in high-level sensory regions of the brain remain enigmatic because of the many nonlinear transformations that separate responses of these neurons from the input stimuli. One would like to have dimensionality reduction methods that can describe responses of such neurons in terms of operations on a large but still manageable set of relevant input features. A number of methods have been developed for this purpose, but often these methods rely on the expansion of the input space to capture as many relevant stimulus components as statistically possible. This expansion leads to a lower effective sampling thereby reducing the accuracy of the estimated components. Alternatively, so-called low-rank methods explicitly search for a small number of component

SUBMITTER: Kaardal JT 

PROVIDER: S-EPMC5534486 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature