Derivatives and inverse of cascaded linear+nonlinear neural models.

ABSTRACT: In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences. However, the conventional literature is usually too focused on only describing the forward input-output transform. Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse. The fundamental reason for this analytical treatment is that it offers useful analytical insight into the psychophysics, the physiology, and the function of the visual system. For instance, we show how the trends of the sensitivity (volume of the discrimination regions) and the adaptation of the receptive fields can be identified in the expression of the Jacobian w.r.t. the stimulus. This matrix also tells us whi

SUBMITTER: Martinez-Garcia M 

PROVIDER: S-EPMC6188639 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature