Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures

TL;DR

This paper introduces a Bayesian bi-clustering method for neural spiking data that captures spatial and temporal structures through latent trajectories and dynamics, enabling more accurate analysis of large-scale neural recordings.

Contribution

The paper develops a non-parametric Bayesian bi-clustering model with an efficient MCMC algorithm for analyzing neural spiking activity, capturing complex latent structures.

Findings

Successfully recovers true bi-clustering structures in simulations.

Provides more accurate and interpretable results in neural data analysis.

Potential applications beyond neuroscience.

Abstract

Modern neural recording techniques allow neuroscientists to obtain spiking activity of multiple neurons from different brain regions over long time periods, which requires new statistical methods to be developed for understanding structure of the large-scale data. In this paper, we develop a bi-clustering method to cluster the neural spiking activity 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 (populationally) synchronous local linear dynamics shared with different periods. To flexibly extract 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 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 result. Overall, the proposed method provides scientific insights for large-scale (counting) time series with elongated recording periods, and it can potentially have application beyond neuroscience.