  This paper describes a framework for the development of an integrative
cognitive system based on probabilistic generative models (PGMs) called
Neuro-SERKET. Neuro-SERKET is an extension of SERKET, which can compose
elemental PGMs developed in a distributed manner and provide a scheme that
allows the composed PGMs to learn throughout the system in an unsupervised way.
In addition to the head-to-tail connection supported by SERKET, Neuro-SERKET
supports tail-to-tail and head-to-head connections, as well as neural
network-based modules, i.e., deep generative models. As an example of a
Neuro-SERKET application, an integrative model was developed by composing a
variational autoencoder (VAE), a Gaussian mixture model (GMM), latent Dirichlet
allocation (LDA), and automatic speech recognition (ASR). The model is called
VAE+GMM+LDA+ASR. The performance of VAE+GMM+LDA+ASR and the validity of
Neuro-SERKET were demonstrated through a multimodal categorization task using
image data and a speech signal of numerical digits.
