  In human perception and cognition, a fundamental operation that brains
perform is interpretation: constructing coherent neural states from noisy,
incomplete, and intrinsically ambiguous evidence. The problem of interpretation
is well matched to an early and often overlooked architecture, the attractor
network---a recurrent neural net that performs constraint satisfaction,
imputation of missing features, and clean up of noisy data via energy
minimization dynamics. We revisit attractor nets in light of modern deep
learning methods and propose a convolutional bipartite architecture with a
novel training loss, activation function, and connectivity constraints. We
tackle larger problems than have been previously explored with attractor nets
and demonstrate their potential for image completion and super-resolution. We
argue that this architecture is better motivated than ever-deeper feedforward
models and is a viable alternative to more costly sampling-based generative
methods on a range of supervised and unsupervised tasks.
