Papers › Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient)...
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. Our proposed RNN is based on a time-discretization of a system of second-order ordinary differential equations, modeling networks of controlled nonlinear oscillators. We prove precise bounds on the gradients of the hidden states, leading to the mitigation of the exploding and vanishing gradient problem for this RNN. Experiments show that the proposed RNN is comparable in performance to the state of the art on a variety of benchmarks, demonstrating the potential of this architecture to provide stable and accurate RNNs for processing complex sequential data.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | IMDb | coRNN | Accuracy | 87.4% | #43 of 49 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | coRNN | Permuted Accuracy | 97.34% | #11 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | coRNN | Unpermuted Accuracy | 99.4% | #11 of 30 | Archive leaderboard | report |
| Sequential Image Classification | noise padded CIFAR-10 | coRNN | % Test Accuracy | 59.0 | #4 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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