Topic: binary cross-entropy loss

Recently, there has been an increase in interest in exploring the hypothesis that neural activity conveys information through precise spiking motifs. To investigate this phenomenon, several algorithms have been proposed to detect such motifs in Single Unit Activity recorded from populations of neurons. Based on the inversion of a generative model of raster plot synthesis, we present a novel detection model. This model derives an optimal detection procedure in the form of logistic regression combined with temporal convolution. Its differentiability allows for a supervised learning approach usin