mlsanityData to 7 Oct 2026

Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing

arXiv 2407.06452 ↗First posted 8 Jul 2024cs.NE cs.AIcited by 2

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Abstract (arXiv)

Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves together three groundbreaking studies that revolutionize SNN performance through the introduction of heterogeneity in neuron and synapse dynamics. We explore the transformative impact of Heterogeneous Recurrent Spiking Neural Networks (HRSNNs), supported by rigorous analytical frameworks and novel pruning methods like Lyapunov Noise Pruning (LNP). Our findings reveal how heterogeneity not only enhances classification performance but also reduces spiking activity, leading to more efficient and robust networks. By bridging theoretical insights with practical applications, this comprehensive summary highlights the potential of SNNs to outperform traditional neural networks while maintaining lower computational costs. Join us on a journey through the cutting-edge advancements that pave the way for the future of intelligent, energy-efficient neural computing.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 92 references that are in the map. First posted on arXiv 8 Jul 2024.

Builds on

41 of 92 references.

References · 41

  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  2. 2018A Tutorial on Bayesian Optimizationcited by 734
  3. 2017Deep Neural Networks as Gaussian Processescited by 617
  4. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  5. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  6. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
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29 more
  1. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  2. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 135
  3. 2019Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamicscited by 132
  4. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 98
  5. 2020Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networkscited by 43
  6. 2019Spiking Neural Networks and Online Learning: An Overview and Perspectivescited by 41
  7. 2021Pruning of Deep Spiking Neural Networks through Gradient Rewiringcited by 41
  8. 2019Comprehensive SNN Compression Using ADMM Optimization and Activity Regularizationcited by 38
  9. 2021Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timecited by 33
  10. 2020On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Toolscited by 31
  11. 2021A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detectioncited by 31
  12. 2018Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networkscited by 30
  13. 2022Exploring Lottery Ticket Hypothesis in Spiking Neural Networkscited by 25
  14. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 22
  15. 2023ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networkscited by 22
  16. 2019ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computingcited by 12
  17. 2021Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticitycited by 12
  18. 2021Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptationcited by 11
  19. 2022Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classificationcited by 11
  20. 2020Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacitycited by 10
  21. 2024Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNNcited by 10
  22. 2021Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networkscited by 8
  23. 2023Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principlescited by 8
  24. 2017Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Modelcited by 7
  25. 2021Interacting Hawkes processes with multiplicative inhibitioncited by 6
  26. 2023Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Predictioncited by 5
  27. 2024Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networkscited by 3
  28. 2022Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Trainingcited by 2
  29. 2023Unsupervised 3D Object Learning through Neuron Activity aware Plasticitycited by 2

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networkscited by 1
  2. 2025FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learningcited by 1