English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is well established that temporal organization is critical to memory, andthat the ability to temporally organize information is fundamental to manyperceptual, cognitive, and motor processes. While our understanding of how thebrain processes the spatial context of memories has advanced considerably, ourunderstanding of their temporal organization lags far behind. In this paper, wepropose a new approach for elucidating the neural basis of complex behaviorsand temporal organization of memories. More specifically, we focus on neuraldecoding - the prediction of behavioral or experimental conditions based onobserved neural data. In general, this is a challenging classification problem,which is of immense interest in neuroscience. Our goal is to develop a newframework that not only improves the overall accuracy of decoding, but alsoprovides a clear latent representation of the decoding process. To accomplishthis, our approach uses a Variational Auto-encoder (VAE) model with adiversity-encouraging prior based on determinantal point processes (DPP) toimprove latent representation learning by avoiding redundancy in the latentspace. We apply our method to data collected from a novel rat experiment thatinvolves presenting repeated sequences of odors at a single port and testingthe rats' ability to identify each odor. We show that our method leads tosubstantially higher accuracy rate for neural decoding and allows to discovernovel biological phenomena by providing a clear latent representation of thedecoding process.

Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method