  Deep learning has seen a movement away from representing examples with a
monolithic hidden state towards a richly structured state. For example,
Transformers segment by position, and object-centric architectures decompose
images into entities. In all these architectures, interactions between
different elements are modeled via pairwise interactions: Transformers make use
of self-attention to incorporate information from other positions;
object-centric architectures make use of graph neural networks to model
interactions among entities. However, pairwise interactions may not achieve
global coordination or a coherent, integrated representation that can be used
for downstream tasks. In cognitive science, a global workspace architecture has
been proposed in which functionally specialized components share information
through a common, bandwidth-limited communication channel. We explore the use
of such a communication channel in the context of deep learning for modeling
the structure of complex environments. The proposed method includes a shared
workspace through which communication among different specialist modules takes
place but due to limits on the communication bandwidth, specialist modules must
compete for access. We show that capacity limitations have a rational basis in
that (1) they encourage specialization and compositionality and (2) they
facilitate the synchronization of otherwise independent specialists.
