mlsanityData to 7 Oct 2026

Noise-Robust Deep Spiking Neural Networks with Temporal Information

arXiv 2104.11169 ↗First posted 22 Apr 2021cs.NE cs.AI cs.LGcited by 5

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Abstract (arXiv)

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 22 Apr 2021.

Builds on

12 of 32 references.

References · 12

  1. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  2. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  3. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 141
  4. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 98
  5. 2020T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Codingcited by 45
  6. 2018Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networkscited by 34
  7. 2015Stochastic Synapses Enable Efficient Brain-Inspired Learning Machinescited by 21
  8. 2020Towards Understanding the Effect of Leak in Spiking Neural Networkscited by 15
  9. 2018Demonstrating Advantages of Neuromorphic Computation: A Pilot Studycited by 14
  10. 2020Spiking Neural Networks Hardware Implementations and Challenges: a Surveycited by 14
  11. 2019Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learningcited by 3
  12. 2020Synaptic Learning with Augmented Spikescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2; 2026: 1.

5 papers.

  1. 2024Balanced Resonate-and-Fire Neuronscited by 12
  2. 2025Spike-TBR: a Noise Resilient Neuromorphic Event Representationcited by 1
  3. 2022Exploring Tradeoffs in Spiking Neural Networkscited by 0
  4. 2025Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithmscited by 0
  5. 2026Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Roboticscited by 0