  Biological visual systems exhibit abundant recurrent connectivity.
State-of-the-art neural network models for visual recognition, by contrast,
rely heavily or exclusively on feedforward computation. Any finite-time
recurrent neural network (RNN) can be unrolled along time to yield an
equivalent feedforward neural network (FNN). This important insight suggests
that computational neuroscientists may not need to engage recurrent
computation, and that computer-vision engineers may be limiting themselves to a
special case of FNN if they build recurrent models. Here we argue, to the
contrary, that FNNs are a special case of RNNs and that computational
neuroscientists and engineers should engage recurrence to understand how brains
and machines can (1) achieve greater and more flexible computational depth, (2)
compress complex computations into limited hardware, (3) integrate priors and
priorities into visual inference through expectation and attention, (4) exploit
sequential dependencies in their data for better inference and prediction, and
(5) leverage the power of iterative computation.
