David Liu

This paper introduces a Bayesian nonparametric method to model neural spike train variability, showing improved performance in capturing interspike interval statistics compared to existing approaches.

The paper extends the manifold Gaussian process latent variable model to handle non-Euclidean neural data with temporal continuity and count-based noise, improving performance on synthetic and real neural recordings.

This paper introduces a probabilistic spike count model to capture neural variability across time and covariates, finding that variability is modulated by external factors and explained by latent factors.