mlsanityData to 14 Oct 2026

Deep Oscillatory Neural Network

arXiv 2405.03725 ↗First posted 6 May 2024cs.NE cs.AI cs.LGcited by 3

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Abstract (arXiv)

We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity. With this motivation, the DONN is designed to have oscillatory internal dynamics. Neurons of the DONN are either nonlinear neural oscillators or traditional neurons with sigmoidal or ReLU activation. The neural oscillator used in the model is the Hopf oscillator, with the dynamics described in the complex domain. Input can be presented to the neural oscillator in three possible modes. The sigmoid and ReLU neurons also use complex-valued extensions. All the weight stages are also complex-valued. Training follows the general principle of weight change by minimizing the output error and therefore has an overall resemblance to complex backpropagation. A generalization of DONN to convolutional networks known as the Oscillatory Convolutional Neural Network is also proposed. The two proposed oscillatory networks are applied to a variety of benchmark problems in signal and image/video processing. The performance of the proposed models is either comparable or superior to published results on the same data sets.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 106 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

14 of 106 references.

References · 14

  1. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  3. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
  4. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  5. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  6. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  7. 2024SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networkscited by 40
  8. 2017Music Generation by Deep Learning - Challenges and Directionscited by 24
  9. 2023High-performance deep spiking neural networks with 0.3 spikes per neuroncited by 22
  10. 2020Neural Machine Translation: A Review of Methods, Resources, and Toolscited by 19
  11. 2018Learning to match transient sound events using attentional similarity for few-shot sound recognitioncited by 13
  12. 2019A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNscited by 10
2 more
  1. 2024Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networkscited by 10
  2. 2020Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Phasor Agents: Oscillatory Graphs with Three-Factor Plasticity and Sleep-Staged Learningcited by 0
  2. 2026Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networkscited by 0
  3. 2026Frequency-Based Reservoir computingcited by 0