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To push forward the important emerging research field surrounding multi-arearecurrent neural networks (RNNs), we expand theoretically and empirically onthe provably stable RNNs of RNNs introduced by Kozachkov et al. in "RNNs ofRNNs: Recursive Construction of Stable Assemblies of Recurrent NeuralNetworks". We prove relaxed stability conditions for salient special cases ofthis architecture, most notably for a global workspace modular structure. Wethen demonstrate empirical success for Global Workspace Sparse Combo Nets witha small number of trainable parameters, not only through strong overall testperformance but also greater resilience to removal of individual subnetworks.These empirical results for the global workspace inter-area topology arecontingent on stability preservation, highlighting the relevance of ourtheoretical work for enabling modular RNN success. Further, by exploringsparsity in the connectivity structure between different subnetwork modulesmore broadly, we improve the state of the art performance for stable RNNs onbenchmark sequence processing tasks, thus underscoring the general utility ofspecialized graph structures for multi-area RNNs.

Contraction Properties of the Global Workspace Primitive