Preprocessing Data of Varying Trial Duration with Linear Time Warping to Extend on the Applicability of SNP-GPFA

Signal-noise Poisson-spiking Gaussian Process Factor Analysis is a popular model for analyzing neuroscience data. However, a key limitation exists, in that it cannot be applied to data of varying trial duration, limiting the range of experiments that can be performed. This work proposes data preprocessing techniques to feature align uneven length spike data, as well as findings from the application of SNP-GPFA to transformed rodent V1 data. We find that stretching followed by linear time warping is sufficient to align rodent V1 data in time and with respect to a paired visual stimulus and reward feature for successful application of SNP-GPFA.