  Non-parametric models, such as Gaussian Processes (GP), show promising
results in the analysis of complex data. Their applications in neuroscience
data have recently gained traction. In this research, we introduce a novel
neural decoder model built upon GP models. The core idea is that two GPs
generate neural data and their associated labels using a set of low-dimensional
latent variables. Under this modeling assumption, the latent variables
represent the underlying manifold or essential features present in the neural
data. When GPs are trained, the latent variable can be inferred from neural
data to decode the labels with a high accuracy. We demonstrate an application
of this decoder model in a verbal memory experiment dataset and show that the
decoder accuracy in predicting stimulus significantly surpasses the
state-of-the-art decoder models. The preceding performance of this model
highlights the importance of utilizing non-parametric models in the analysis of
neuroscience data.
