  The current state-of-the-art object recognition algorithms, deep
convolutional neural networks (DCNNs), are inspired by the architecture of the
mammalian visual system, and are capable of human-level performance on many
tasks. However, even these algorithms make errors. As they are trained for
object recognition tasks, it has been shown that DCNNs develop hidden
representations that resemble those observed in the mammalian visual system.
Moreover, DCNNs trained on object recognition tasks are currently among the
best models we have of the mammalian visual system. This led us to hypothesize
that teaching DCNNs to achieve even more brain-like representations could
improve their performance. To test this, we trained DCNNs on a composite task,
wherein networks were trained to: a) classify images of objects; while b)
having intermediate representations that resemble those observed in neural
recordings from monkey visual cortex. Compared with DCNNs trained purely for
object categorization, DCNNs trained on the composite task had better object
recognition performance and are more robust to label corruption. Interestingly,
we also found that neural data was not required, but randomized data with the
same statistics as neural data also boosted performance. Our results outline a
new way to train object recognition networks, using strategies in which the
brain - or at least the statistical properties of its activation patterns -
serves as a teacher signal for training DCNNs.
