  Probabilistic topic models are popular unsupervised learning methods,
including probabilistic latent semantic indexing (pLSI) and latent Dirichlet
allocation (LDA). By now, their training is implemented on general purpose
computers (GPCs), which are flexible in programming but energy-consuming.
Towards low-energy implementations, this paper investigates their training on
an emerging hardware technology called the neuromorphic multi-chip systems
(NMSs). NMSs are very effective for a family of algorithms called spiking
neural networks (SNNs). We present three SNNs to train topic models. The first
SNN is a batch algorithm combining the conventional collapsed Gibbs sampling
(CGS) algorithm and an inference SNN to train LDA. The other two SNNs are
online algorithms targeting at both energy- and storage-limited environments.
The two online algorithms are equivalent with training LDA by using
maximum-a-posterior estimation and maximizing the semi-collapsed likelihood,
respectively. They use novel, tailored ordinary differential equations for
stochastic optimization. We simulate the new algorithms and show that they are
comparable with the GPC algorithms, while being suitable for NMS
implementation. We also propose an extension to train pLSI and a method to
prune the network to obey the limited fan-in of some NMSs.
