Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data
This work addresses the challenge of accurately decoding neural data for neuroscience applications, representing an incremental improvement over existing methods.
The authors tackled the problem of decoding complex neural data by introducing a novel neural decoder model based on Gaussian Processes, which uses latent variables to represent underlying features, and demonstrated that it significantly surpasses state-of-the-art models in predicting stimuli in a verbal memory experiment dataset.
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.