# SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron

arXiv: https://arxiv.org/abs/1903.02440 · HTML: https://mlsanity.com/paper/1903.02440/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

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

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Q-SpiNN: A Framework for Quantizing Spiking Neural Networks](https://mlsanity.com/paper/2107.01807/) (2021)
- [Recent Advances and New Frontiers in Spiking Neural Networks](https://mlsanity.com/paper/2204.07050/) (2022)
- [A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices](https://mlsanity.com/paper/2303.10780/) (2023)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Learning through structure: towards deep neuromorphic knowledge graph embeddings](https://mlsanity.com/paper/2109.10376/) (2021)
- [Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario](https://mlsanity.com/paper/2207.04881/) (2022)
- [EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems](https://mlsanity.com/paper/2304.04039/) (2023)
- [RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults](https://mlsanity.com/paper/2304.04041/) (2023)
- [2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition](https://mlsanity.com/paper/2205.13474/) (2022)
- [Spiking Approximations of the MaxPooling Operation in Deep SNNs](https://mlsanity.com/paper/2205.07076/) (2022)
- [QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models](https://mlsanity.com/paper/2601.00679/) (2026)
- [Models Developed for Spiking Neural Networks](https://mlsanity.com/paper/2212.04377/) (2022)
- [Continual Learning with Columnar Spiking Neural Networks](https://mlsanity.com/paper/2506.17169/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Multi-GPU SNN Simulation with Static Load Balancing](https://mlsanity.com/paper/2102.04681/) (2021)
- [Even Faster SNN Simulation with Lazy+Event-driven Plasticity and Shared Atomics](https://mlsanity.com/paper/2107.04092/) (2021)
- [Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents](https://mlsanity.com/paper/2504.13541/) (2025)
- [Spyker: High-performance Library for Spiking Deep Neural Networks](https://mlsanity.com/paper/2301.13659/) (2023)
- [Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch](https://mlsanity.com/paper/2302.07624/) (2023)
- [SpikeVox: Towards Energy-Efficient Speech Therapy Framework with Spike-driven Generative Language Models](https://mlsanity.com/paper/2510.15566/) (2025)
- [PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers](https://mlsanity.com/paper/2606.03257/) (2026)
- [PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers](https://mlsanity.com/paper/2606.03428/) (2026)

## Abstract (arXiv)

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of the existing SNN simulation frameworks are not practically efficient enough for large-scale AI tasks. In this paper, we introduce SpykeTorch, an open-source high-speed simulation framework based on PyTorch. This framework simulates convolutional SNNs with at most one spike per neuron and the rank-order encoding scheme. In terms of learning rules, both spike-timing-dependent plasticity (STDP) and reward-modulated STDP (R-STDP) are implemented, but other rules could be implemented easily. Apart from the aforementioned properties, SpykeTorch is highly generic and capable of reproducing the results of various studies. Computations in the proposed framework are tensor-based and totally done by PyTorch functions, which in turn brings the ability of just-in-time optimization for running on CPUs, GPUs, or Multi-GPU platforms.