ArticleFrontiers in computational neuroscience2021
Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.Nature neuroscience · 2024Article
- Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach.Sensors (Basel, Switzerland) · 2023Article
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- Category learning in a recurrent neural network with reinforcement learning.Frontiers in psychiatry · 2022Article
- Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.Neural computation · 2021Article
- Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed.Entropy (Basel, Switzerland) · 2020Article
- Universality and individuality in neural dynamics across large populations of recurrent networks.Advances in neural information processing systems · 2019Article
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Authors and funding
3 authors.
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Abstract
Gated recurrent units (GRUs) are specialized memory elements for building recurrent neural networks. Despite their incredible success on various tasks, including extracting dynamics underlying neural data, little is understood about the specific dynamics representable in a GRU network. As a result, it is both difficult to know a priori how successful a GRU network will perform on a given task, and also their capacity to mimic the underlying behavior of their biological counterparts. Using a continuous time analysis, we gain intuition on the inner workings of GRU networks. We restrict our presentation to low dimensions, allowing for a comprehensive visualization. We found a surprisingly rich repertoire of dynamical features that includes stable limit cycles (nonlinear oscillations), multi-stable dynamics with various topologies, and homoclinic bifurcations. At the same time we were unable to train GRU networks to produce continuous attractors, which are hypothesized to exist in biological neural networks. We contextualize the usefulness of different kinds of observed dynamics and support our claims experimentally.
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