Incorporating Task-Related Information in Dimensionality Reduction of Neural Population Using Autoencoders

Qi Lian, Yunzhu Liu, Yu Zhao, Yu Qi

Abstract: Dimensionality reduction plays an important role in neural signal analysis. Most dimensionality reduction methods can effectively describe the majority of the variance of the data, such as principal component analysis (PCA) and locally linear embedding (LLE). However, they may not be able to capture useful information given a specific task, since these approaches are unsupervised. This study proposes an autoencoder-based approach that incorporates task-related information as strong guidance to the dimensionality reduction process, such that the low dimensional representations can better reflect information directly related to the task. Experimental results show that the proposed method is capable of finding task-related features of the neural population effectively.