Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data

TL;DR

This paper introduces a novel Gaussian Process-based neural decoder that uses latent variables to effectively analyze complex neural data, significantly improving decoding accuracy in neuroscience experiments.

Contribution

The paper presents a new GP-based neural decoding model leveraging latent variables to capture essential features, outperforming existing decoders in neural data analysis.

Findings

Decoder significantly outperforms state-of-the-art models in predicting stimuli.

Latent variables effectively capture underlying neural data features.

Non-parametric GP models are highly effective for neuroscience data analysis.

Abstract

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.