# Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction

arXiv: https://arxiv.org/abs/2003.12346 · HTML: https://mlsanity.com/paper/2003.12346/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors](https://mlsanity.com/paper/2211.09648/) (2022)
- [Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition](https://mlsanity.com/paper/2203.12247/) (2022)
- [Training Full Spike Neural Networks via Auxiliary Accumulation Pathway](https://mlsanity.com/paper/2301.11929/) (2023)
- [SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition](https://mlsanity.com/paper/2308.04369/) (2023)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks](https://mlsanity.com/paper/2305.16044/) (2023)
- [TTPOINT: A Tensorized Point Cloud Network for Lightweight Action Recognition with Event Cameras](https://mlsanity.com/paper/2308.09993/) (2023)
- [Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks](https://mlsanity.com/paper/2411.04760/) (2024)
- [A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning](https://mlsanity.com/paper/2111.00791/) (2021)
- [ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition](https://mlsanity.com/paper/2409.01564/) (2024)
- [SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection](https://mlsanity.com/paper/2311.06570/) (2023)
- [A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks](https://mlsanity.com/paper/2210.00894/) (2022)
- [SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2404.00383/) (2024)
- [Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks](https://mlsanity.com/paper/2505.05375/) (2025)
- [SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks](https://mlsanity.com/paper/2505.12292/) (2025)
- [Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks](https://mlsanity.com/paper/2510.06629/) (2025)
- [EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm](https://mlsanity.com/paper/2402.10078/) (2024)
- [Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm](https://mlsanity.com/paper/2404.18066/) (2024)
- [Using CSNNs to Perform Event-based Data Processing & Classification on ASL-DVS](https://mlsanity.com/paper/2408.00611/) (2024)
- [SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities](https://mlsanity.com/paper/2507.16151/) (2025)

## Abstract (arXiv)

Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to conventional artificial neural networks (ANNs), while preserving ANN's properties. However, temporal coding in layers of convolutional spiking neural networks and other types of SNNs has yet to be studied. In this paper, we provide insight into spatio-temporal feature extraction of convolutional SNNs in experiments designed to exploit this property. The shallow convolutional SNN outperforms state-of-the-art spatio-temporal feature extractor methods such as C3D, ConvLstm, and similar networks. Furthermore, we present a new deep spiking architecture to tackle real-world problems (in particular classification tasks) which achieved superior performance compared to other SNN methods on NMNIST (99.6%), DVS-CIFAR10 (69.2%) and DVS-Gesture (96.7%) and ANN methods on UCF-101 (42.1%) and HMDB-51 (21.5%) datasets. It is also worth noting that the training process is implemented based on variation of spatio-temporal backpropagation explained in the paper.