Explaining the Human Visual Brain Challenge 2019 -- receptive fields and surrogate features

In this paper I review the submission to the Explaining the Human Visual Brain Challenge 2019 in both the fMRI and MEG tracks. The goal was to construct neural network features which generate the so-called representational dissimilarity matrix (RDM) which is most similar to the one extracted from fMRI and MEG data upon viewing a set of images. I review exploring the optimal granularity of the receptive field, a construction of intermediate surrogate features using Multidimensional Scaling and modelling them using neural network features. I also point out some peculiarities of the RDM construction which have to be taken into account.