  Deep predictive coding networks are neuroscience-inspired unsupervised
learning models that learn to predict future sensory states. We build upon the
PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if
predictive coding representations are useful to predict brain activity in the
visual cortex. We use representational similarity analysis (RSA) to compare
PredNet representations to functional magnetic resonance imaging (fMRI) and
magnetoencephalography (MEG) data from the Algonauts Project. In contrast to
previous findings in the literature (Khaligh-Razavi &Kriegeskorte, 2014), we
report empirical data suggesting that unsupervised models trained to predict
frames of videos may outperform supervised image classification baselines. Our
best submission achieves an average noise normalized score of 16.67% and 27.67%
on the fMRI and MEG tracks of the Algonauts Challenge.
