  With advances in neural recording techniques, neuroscientists are now able to
record the spiking activity of many hundreds of neurons simultaneously, and new
statistical methods are needed to understand the structure of this large-scale
neural population activity. Although previous work has tried to summarize
neural activity within and between known populations by extracting
low-dimensional latent factors, in many cases what determines a unique
population may be unclear. Neurons differ in their anatomical location, but
also, in their cell types and response properties. To identify populations
directly related to neural activity, we develop a clustering method based on a
mixture of dynamic Poisson factor analyzers (mixDPFA) model, with the number of
clusters and dimension of latent factors for each cluster treated as unknown
parameters. To analyze the proposed mixDPFA model, we propose a Markov chain
Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution.
Validating our proposed MCMC algorithm through simulations, we find that it can
accurately recover the unknown parameters and the true clustering in the model,
and is insensitive to the initial cluster assignments. We then apply the
proposed mixDPFA model to multi-region experimental recordings, where we find
that the proposed method can identify novel, reliable clusters of neurons based
on their activity, and may, thus, be a useful tool for neural data analysis.
