ArticleScience advances2023
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Interpretable machine learning reveals hemispheric asymmetry of state switching in the suprachiasmatic nucleus.Patterns (New York, N.Y.) · 2026Article
- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Anion-Engineered Organic Electrochemical Transistors With Multi-Timescale Synaptic Dynamics for Task-Adaptive Spiking Neural Networks.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Artificial sparse neuron dendrites for visual information inference.Science advances · 2026Article
- Systematic Evaluation of Biologically Inspired Motion Detection Models: From LGMD and EMD to Hybrid Spiking Neural Networks.Biomimetics (Basel, Switzerland) · 2026Article
- SE-SNN: Squeeze-and-Excitation-Enhanced Spiking Neural Networks with Learnable Neuron Dynamics for Event-Based Vision.Biomimetics (Basel, Switzerland) · 2026Article
- UDC-SNN: An Uncertainty-Aware Dynamic Cascading Framework with Spiking Neural Network for Balancing Performance and Energy in Multimodal Emotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- Event-Based Vision at the Edge: A Review.Brain sciences · 2026Review
- PS-SNN: pattern separation learning for expandable spiking neural networks in class-incremental learning.Scientific reports · 2026Article
- DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset.Scientific data · 2026Article
- Leaky-Integrate-Fire Neuron via Synthetic Antiferromagnetic Coupling and Spin-Orbit Torque.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Event-Driven Spiking Neural Networks for Private Vehicle Parking Prediction.Entropy (Basel, Switzerland) · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Homogeneous integration of two-dimensional material-based optoelectronic neurons and ferroelectric synapses for neuromorphic vision.Nature communications · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Exploring Quantum-Inspired Encoding Strategies in Neuromorphic Systems for Affective State Recognition.Sensors (Basel, Switzerland) · 2026Article
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation.Frontiers in artificial intelligence · 2026Article
- A compressed sensing neuromorphic processor for sparse signal classification.Frontiers in neuroscience · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.