  Similarity analysis is one of the crucial steps in most fMRI studies.
Representational Similarity Analysis (RSA) can measure similarities of neural
signatures generated by different cognitive states. This paper develops Deep
Representational Similarity Learning (DRSL), a deep extension of RSA that is
appropriate for analyzing similarities between various cognitive tasks in fMRI
datasets with a large number of subjects, and high-dimensionality -- such as
whole-brain images. Unlike the previous methods, DRSL is not limited by a
linear transformation or a restricted fixed nonlinear kernel function -- such
as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping
neural responses to linear space, where this network can implement a customized
nonlinear transformation for each subject separately. Furthermore, utilizing a
gradient-based optimization in DRSL can significantly reduce runtime of
analysis on large datasets because it uses a batch of samples in each iteration
rather than all neural responses to find an optimal solution. Empirical studies
on multi-subject fMRI datasets with various tasks -- including visual stimuli,
decision making, flavor, and working memory -- confirm that the proposed method
achieves superior performance to other state-of-the-art RSA algorithms.
