The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction
Fig 1
The linear-nonlinear-Poisson (LNP) encoding model formalizes the neural encoding process in terms of a cascade of three stages.
First, the high-dimensional stimulus s projects onto bank of filters contained in the columns of a matrix K, resulting in a point in a low-dimensional neural feature space K⊤s. Second, an instantaneous nonlinear function f maps the filtered stimulus to an instantaneous spike rate λ. Third, spikes r are generated according to an inhomogeneous Poisson process.