Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures

Modern neural recording techniques allow neuroscientists to obtain spiking activity from many hundreds of neurons simultaneously over long time periods, and new statistical methods are needed to understand structure of the large-scale data, in terms of both neuron numbers and recording duration. Here, we develop a bi-clustering method to cluster the neural spiking activity both spatially and temporally, according to their low-dimensional latent structures. The spatial (neuron) clusters are defined by the latent trajectories within each neural population, while the temporal (state) clusters are defined by local linear dynamics manner across the population. To flexibly extracting the bi-clustering structure, we build the model non-parametrically, and develop an efficient Markov chain Monte Carlo (MCMC) algorithm to sample the posterior distributions of model parameters. Validating our proposed MCMC algorithm through simulations, we find the method can recover unknown parameters and true bi-clustering structures successfully. We then apply the proposed bi-clustering method for counting series to multi-regional neural recordings under different experiment settings, where we find that simultaneously considering latent trajectories and spatial-temporal clustering structures can provide us with a more accurate and interpretable results. Overall, the proposed method provides scientific insights for large-scale (counting) time series with elongated recording periods, and it can have application beyond neuroscience.