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.

该研究引入了一种基于高斯过程模型的新型神经解码器模型，通过两个高斯过程生成神经数据及其相关标签，并使用一组低维潜变量表示神经数据中的潜在流形或重要特征，从而高精度地从神经数据中推断潜变量以解码标签，在口头记忆实验数据集中的应用表明该解码器模型在预测刺激方面的准确度显著超过了现有的解码器模型，强调了在神经科学数据分析中利用非参数模型的重要性。

潜变量双高斯过程模型用于解码复杂神经数据