  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.
