  Much work has been done refining and characterizing the receptive fields
learned by deep learning algorithms. A lot of this work has focused on the
development of Gabor-like filters learned when enforcing sparsity constraints
on a natural image dataset. Little work however has investigated how these
filters might expand to the temporal domain, namely through training on natural
movies. Here we investigate exactly this problem in established temporal deep
learning algorithms as well as a new learning paradigm suggested here, the
Temporal Autoencoding Restricted Boltzmann Machine (TARBM).
