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Gaussian-Process Factor Analysis (GPFA) is a useful method to discover the unknown dynamics of neural activities. Currently, there are a lot of studies based on the GPFA model. However, many of the existing GPFA models are specially for a specific situation, and they are no longer effective in other conditions. This paper aims to solve this problem by proposing a GPFA framework based on the standard GPFA model which can be applied to any neural dynamics with unknown latent structure. This framework also provides an idea to determine the latent dimension by using cross-validation. This framework will first be used on the synthetic data created by a generative model, to test two different ways of reproducing the spike train and check its utility. After that, it will be applied to real neural data recorded from anesthetized macaque monkeys. The framework shows a good result on the synthetic data. And its performance on the real neural data suggests that it still has some space to be improved. Discussion of the result will mainly focus on the potential approach to improve the frameworks accuracy.
Tiancheng Xu (Thu,) studied this question.