# Noise-Robust Deep Spiking Neural Networks with Temporal Information

arXiv: https://arxiv.org/abs/2104.11169 · HTML: https://mlsanity.com/paper/2104.11169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks](https://mlsanity.com/paper/2002.10085/) (2020)
- [T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding](https://mlsanity.com/paper/2003.11741/) (2020)
- [Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks](https://mlsanity.com/paper/1809.03142/) (2018)
- [Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines](https://mlsanity.com/paper/1511.04484/) (2015)
- [Spiking Neural Networks Hardware Implementations and Challenges: a Survey](https://mlsanity.com/paper/2005.01467/) (2020)
- [Towards Understanding the Effect of Leak in Spiking Neural Networks](https://mlsanity.com/paper/2006.08761/) (2020)
- [Demonstrating Advantages of Neuromorphic Computation: A Pilot Study](https://mlsanity.com/paper/1811.03618/) (2018)
- [Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning](https://mlsanity.com/paper/1902.01094/) (2019)
- [Synaptic Learning with Augmented Spikes](https://mlsanity.com/paper/2005.04820/) (2020)

## Built on by (most cited first, 5 of 5)

- [Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2402.14603/) (2024)
- [Spike-TBR: a Noise Resilient Neuromorphic Event Representation](https://mlsanity.com/paper/2506.04817/) (2025)
- [Exploring Tradeoffs in Spiking Neural Networks](https://mlsanity.com/paper/2212.09500/) (2022)
- [Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithms](https://mlsanity.com/paper/2509.07361/) (2025)
- [Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics](https://mlsanity.com/paper/2603.13880/) (2026)

## 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.