A Expression for the marginal and conditional distributions

Hz for scan 2. The recorded visual Hyper-parameters included the learning rate and the regularization coefficient on the readout weights. A single ZIFFA model takes approximately 2-3 hours to train whereas all other models take approximately 20-30 minutes to train. The hy-perparameter search was completed using one GPU for a total of ~20 hours. We estimated the posterior mean of the neuron's responses to an image We then inverse-transformed these samples to yield samples in the space of the neural responses. More specifically, for the FA-based models (except ZIFFA, see below), the posterior mean of the neuron's original response to image To ensure that generated Gaussian samples (1) fall in a range where the transformation is invertible and that they (2) cover the most nonlinear part of the transformation, we kept the variances and covariances relatively small and sampled the mean for each neuron in a transform-specific fashion.