RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks

Author name not available (Why is that?)

Publication date: 16 June 2021

Abstract: Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in the direction of studying multiple interacting areas, and RNN theory needs to be likewise extended. We take a constructive approach towards this problem, leveraging tools from nonlinear control theory and machine learning to characterize when combinations of stable RNNs will themselves be stable. Importantly, we derive conditions which allow for massive feedback connections between interacting RNNs. We parameterize these conditions for easy optimization using gradient-based techniques, and show that stability-constrained "networks of networks" can perform well on challenging sequential-processing benchmark tasks. Altogether, our results provide a principled approach towards understanding distributed, modular function in the brain.

Has companion code repository: https://github.com/ennisthemennis/sparse-combo-net

This page was built for publication: RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6370478)