mlsanityData to 7 Oct 2026

Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks

arXiv 2305.16044 ↗First posted 25 May 2023cs.NE cs.AI cs.LGcited by 17

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Abstract (arXiv)

Formal version available at https://cell.com/patterns/fulltext/S2666-3899(23)00200-3 Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation. We demonstrate that NSNN leads to spiking neural models with competitive performance, improved robustness against challenging perturbations than deterministic SNNs, and better reproducing probabilistic computations in neural coding. This study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 59 of its 193 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

59 of 193 references.

References · 59

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 5; 2026: 2.

17 papers.

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  1. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  2. 2024Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signalscited by 9
  3. 2023Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenescited by 4
  4. 2023SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Networkcited by 4
  5. 2025Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexitycited by 4
  6. 2024Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Modelcited by 3
  7. 2024Noisy Spiking Actor Network for Explorationcited by 2
  8. 2024Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networkscited by 2
  9. 2025Online Continual Learning via Spiking Neural Networks with Sleep Enhanced Latent Replaycited by 2
  10. 2024Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networkscited by 1
  11. 2024The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learningcited by 1
  12. 2026Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networkscited by 1
5 more
  1. 2023Noise Adaptor in Spiking Neural Networkscited by 0
  2. 2025A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networkscited by 0
  3. 2025Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithmscited by 0
  4. 2025A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptationcited by 0
  5. 2026Noisy group neurons with synchronous resetting for high-performance spiking neural networkscited by 0