  It is well established that temporal organization is critical to memory, and
that the ability to temporally organize information is fundamental to many
perceptual, cognitive, and motor processes. While our understanding of how the
brain processes the spatial context of memories has advanced considerably, our
understanding of their temporal organization lags far behind. In this paper, we
propose a new approach for elucidating the neural basis of complex behaviors
and temporal organization of memories. More specifically, we focus on neural
decoding - the prediction of behavioral or experimental conditions based on
observed neural data. In general, this is a challenging classification problem,
which is of immense interest in neuroscience. Our goal is to develop a new
framework that not only improves the overall accuracy of decoding, but also
provides a clear latent representation of the decoding process. To accomplish
this, our approach uses a Variational Auto-encoder (VAE) model with a
diversity-encouraging prior based on determinantal point processes (DPP) to
improve latent representation learning by avoiding redundancy in the latent
space. We apply our method to data collected from a novel rat experiment that
involves presenting repeated sequences of odors at a single port and testing
the rats' ability to identify each odor. We show that our method leads to
substantially higher accuracy rate for neural decoding and allows to discover
novel biological phenomena by providing a clear latent representation of the
decoding process.
