Reviews: Deep Random Splines for Point Process Intensity Estimation of Neural Population Data

This paper proposes a class of random functions where each member is a spline function with the parameters produced by a neural network from Gaussian noise. The first contribution of the paper is the capability of enforcing non-negative constraints over the splines via the alternating projection method over the output of the neural network. The proposed set of spline functions are non-negative and smooth, so they are good candidate to model the intensity functions of temporal point processes. The second contribution of the paper is thus to use smooth non-negative splines to model temporal point processes which makes less strict structural assumptions of the parametric form of the intensity function. Exploring new expressive processes is one of the important problems in the domain of point processes, and this paper advances knowledge in this area.