ArticleFrontiers in neuroscience2019
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A hybrid spiking convolutional neural framework with extreme learning machine for enhanced anomaly detection in network security.Scientific reports · 2026Article
- Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.Sensors (Basel, Switzerland) · 2025Article
- Advancing brain-inspired computing with hybrid neural networks.National science review · 2024Review
- On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks.Scientific reports · 2024Article
- DCENet-based low-light image enhancement improved by spiking encoding and convLSTM.Frontiers in neuroscience · 2024Article
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science advances · 2023Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems.Biomimetics (Basel, Switzerland) · 2023Article
- Spiking CMOS-NVM mixed-signal neuromorphic ConvNet with circuit- and training-optimized temporal subsampling.Frontiers in neuroscience · 2023Article
- Models developed for spiking neural networks.MethodsX · 2023Article
- RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults.Frontiers in neuroscience · 2023Article
- Learning cortical hierarchies with temporal Hebbian updates.Frontiers in computational neuroscience · 2023Article
- SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.Frontiers in neuroscience · 2023Article
- Spiking Neural Networks and Their Applications: A Review.Brain sciences · 2022Review
- EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems.Frontiers in neuroscience · 2022Article
- Detection of COVID-19 from CT scan images: A spiking neural network-based approach.Neural computing & applications · 2021Article
- PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks.Frontiers in neuroinformatics · 2021Article
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4 authors.
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Abstract
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.
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