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

TL;DR

This paper introduces a neural decoding framework using a Variational Auto-encoder with a DPP-based prior, enhancing accuracy and interpretability of neural data related to memory and behavior.

Contribution

It proposes a novel VAE-based method with a diversity-encouraging prior for improved neural decoding and latent representation clarity.

Findings

Higher decoding accuracy on rat odor data

Revealed new biological insights through latent representations

Effective avoidance of redundancy in latent space

Abstract

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.