Awesome SNN Conference Paper

🔥 This repo collects top international conference papers, codes about Spiking Neural Networks for anyone who wants to do research on it. We are continuously improving the project.

The part of 2018-2021 is referenced in Awesome-SNN-Paper-Collection.

The part of 2022 is referenced in 2022年顶会、顶刊SNN相关论文.

Thank the repo or blogs for their contributions to the collection of papers from top conferences or top journals in the SNN field.

🤗 Welcome anyone who is interested to contribute to the repo together ! If you find another papers that are not in this repo, you can pull requests.

❤Thanks so much @Ruichen0424 for the collaboration!

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Table of Contents (Conferences by Year)

1. 2024
   • IJCAI
   • ICML
   • AAAI
   • ICLR
   • CVPR
   • ICASSP
   • PAMI
   • TNNLS
2. 2023
   • ICCV
   • CVPR
   • NeurIPS
   • AAAI
   • ICML
   • ICLR
   • IJCAI
   • ICASSP
   • IJCNN
   • PAMI
   • TNNLS
   • Neural Networks
3. 2022
   • CVPR
   • ECCV
   • NeurIPS
   • AAAI
   • ICASSP
   • ICML
   • IJCAI
   • ICLR
   • IJCNN
   • NEURAL COMPUTATION
   • Neural Networks
   • IEEE TCYB (IEEE Transactions on Cybernetics)
4. 2021
   • NeurIPS
   • CVPR
   • ICLR
   • ICCV
   • ICML
   • IJCAI
   • AAAI
5. 2020
   • NeurIPS
   • CVPR
   • ICLR
   • ECCV
   • ICML
   • IJCAI
   • AAAI
6. 2019
   • NeurIPS
   • ICML
   • IJCAI
   • AAAI
7. 2018
   • NeurIPS
   • ICLR
   • ICML
   • IJCAI
   • AAAI

Abbreviation - Full Name List

Abbreviation	Full Name
CVPR	IEEE Conference on Computer Vision and Pattern Recognition
ICCV	IEEE International Conference on Computer Vision
NeurIPS	Conference on Neural Information Processing Systems
AAAI	Association for the Advancement of Artificial Intelligence
ICLR	International Conference on Learning Representations
ICML	International Conference on Machine Learning
IJCAI	International Joint Conference on Artificial Intelligence
ICASSP	IEEE International Conference on Acoustics, Speech and Signal Processing
IJCNN	International Joint Conference on Neural Networks
PAMI	IEEE Transactions on Pattern Analysis and Machine Intelligence
TNNLS	IEEE Transactions on Neural Networks and Learning Systems

2024

IJCAI-2024

• Learning a Spiking Neural Network for Efficient Image Deraining [arxiv] [paper with code] [code]

• LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization [arxiv] [paper with code]

• TIM: An Efficient Temporal Interaction Module for Spiking Transformer [arxiv] [paper with code] [code]

• One-step Spiking Transformer with a Linear Complexity

• EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices

• Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition [arxiv] [paper with code]

ICML-2024

• CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost [paper]

• SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN [paper] [arxiv] [paper with code] [code]

• NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks [paper]

• Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration [paper]

• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Balanced Resonate-and-Fire Neurons [paper]

• SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms [paper] [arxiv] [paper with code] [code]

• Robust Stable Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation [paper]

• Towards efficient deep spiking neural networks construction with spiking activity based pruning [paper]

• Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning [paper] [arxiv] [paper with code] [code]

• Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network [paper] [arxiv] [paper with code] [code]

• Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Enhancing Adversarial Robustness in SNNs with Sparse Gradients [paper]

AAAI-2024

• Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms [paper]

• An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain [paper] [arxiv] [paper with code] [code]

• Gated Attention Coding for Training High-Performance and Efficient Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Efficient Spiking Neural Networks with Sparse Selective Activation for Continual Learning [paper]

• DeblurSR: Event-Based Motion Deblurring under the Spiking Representation [paper] [arxiv] [paper with code] [code]

• Point-to-Spike Residual Learning for Energy-Efficient 3D Point Cloud Classification [paper]

• Finding Visual Saliency in Continuous Spike Stream [paper] [arxiv] [paper with code] [code]

• Enhancing Training of Spiking Neural Network with Stochastic Latency [paper]

• SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation [paper] [arxiv] [paper with code] [code]

• Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networks [paper] [arxiv]

• Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation [paper] [arxiv] [paper with code] [code]

• Dynamic Spiking Graph Neural Networks [paper] [arxiv] [paper with code]

• Memory-Efficient Reversible Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling [paper] [arxiv] [paper with code] [code]

• Enhancing Representation of Spiking Neural Networks via Similarity-Sensitive Contrastive Learning [paper]

• Dynamic Reactive Spiking Graph Neural Network [paper]

• Transient Glimpses: Unveiling Occluded Backgrounds through the Spike Camera [paper]

• Joint Demosaicing and Denoising for Spike Camera [paper]

• Recognizing Ultra-High-Speed Moving Objects with Bio-Inspired Spike Camera [paper]

• Optical Flow for Spike Camera with Hierarchical Spatial-Temporal Spike Fusion [paper]

• Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training [paper]

ICLR-2024

• Can we get the best of both Binary Neural Networks and Spiking Neural Networks for Efficient Computer Vision? [paper] [openreview]

• LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units [paper] [arxiv] [paper with code] [code] [openreview]

• Threaten Spiking Neural Networks through Combining Rate and Temporal Information [paper] [openreview]

• TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks [paper] [openreview]

• Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks [paper] [openreview]

• Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN [paper] [arxiv] [paper with code] [openreview]

• Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures [paper] [arxiv] [openreview]

• Spatio-Temporal Approximation: A Training-Free SNN Conversion for Transformers [paper] [openreview]

• Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism [paper] [openreview]

• Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings [paper] [arxiv] [paper with code] [code] [openreview]

• Online Stabilization of Spiking Neural Networks [paper] [openreview]

• Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips [paper] [openreview]

• A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model [paper] [openreview]

• Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework [paper] [openreview]

• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition [paper] [arxiv] [paper with code] [openreview]

• EventRPG: Event Data Augmentation with Relevance Propagation Guidance [paper] [code]

CVPR-2024

• SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks [paper] [code]

• Are Conventional SNNs Really Efficient? A Perspective from Network Quantization [paper]

• SFOD: Spiking Fusion Object Detector [paper] [code]

ICASSP-2024

• sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks [paper]

• Optimal ANN-SNN Conversion with Group Neurons [paper] [code]

PAMI-2024

• A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

TNNLS-2024

• Advancing Spiking Neural Networks Toward Deep Residual Learning [paper] [arxiv] [paper with code] [code]

• Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness [paper]

• Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection [paper]

• TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Fully Spiking Actor Network With Intralayer Connections for Reinforcement Learning [paper]

• Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays [paper]

• Efficient Deep Spiking Multilayer Perceptrons With Multiplication-Free Inference [paper]

• CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies [paper]

2023

ICCV-2023

• Efficient Converted Spiking Neural Network for 3D and 2D Classification [paper] [paper with code]

• Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Membrane Potential Batch Normalization for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Deep Directly-Trained Spiking Neural Networks for Object Detection [paper] [arxiv] [paper with code] [code]

• Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence [paper] [arxiv] [paper with code]

• Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation [paper] [paper with code]

• Inherent Redundancy in Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow [paper] [paper with code]

• RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Masked Spiking Transformer [paper] [arxiv] [paper with code] [code]

CVPR-2023

• Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks [paper] [paper with code] [code]

• Constructing Deep Spiking Neural Networks From Artificial Neural Networks With Knowledge Distillation [paper] [arxiv] [paper with code]

• 1000 FPS HDR Video With a Spike-RGB Hybrid Camera [paper] [paper with code]

NeurIPS-2023

• Evolving Connectivity for Recurrent Spiking Neural Networks [paper] [arxiv] [paper with code] [openreview]

• SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes [paper] [arxiv] [paper with code] [openreview]

• Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference [paper] [openreview]

• Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective [paper] [openreview]

• Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension [paper] [arxiv] [paper with code] [code] [openreview]

• EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks [paper] [openreview]

• Spiking PointNet: Spiking Neural Networks for Point Clouds [paper] [arxiv] [paper with code] [code] [openreview]

• Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies [paper] [arxiv] [paper with code] [code] [openreview]

• Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks [paper] [arxiv] [paper with code] [openreview]

• Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons [paper] [arxiv] [paper with code] [code] [openreview]

• SEENN: Towards Temporal Spiking Early Exit Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• Spike-driven Transformer [paper] [arxiv] [paper with code] [code] [openreview]

• SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning [paper] [arxiv] [paper with code] [code] [openreview]

• Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks [paper] [paper with code] [code] [openreview]

• Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability [paper] [openreview]

• Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams [paper] [openreview]

• Trial matching: capturing variability with data-constrained spiking neural networks [paper] [arxiv] [paper with code] [code] [openreview]

• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [paper] [arxiv] [paper with code] [openreview]

• Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes [paper] [paper with code] [code] [openreview]

• Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity [paper] [paper with code] [code] [openreview]

• Direct Training of SNN using Local Zeroth Order Method [paper] [paper with code] [code] [openreview]

• Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera [paper] [arxiv] [paper with code] [openreview]

• SEENN: Towards Temporal Spiking Early-Exit Neural Networks [paper]

AAAI-2023

• Reducing ANN-SNN Conversion Error through Residual Membrane Potential [paper] [arxiv] [paper with code] [code]

• Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse [paper] [arxiv] [paper with code] [code]

• ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks [paper] [arxiv] [paper with code]

• Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition [paper] [arxiv] [paper with code] [code]

• Learning Temporal-Ordered Representation for Spike Streams Based on Discrete Wavelet Transforms [paper]

• SVFI: Spiking-Based Video Frame Interpolation for High-Speed Motion [paper]

• Exploring Temporal Information Dynamics in Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera [paper]

• Learning to Super-resolve Dynamic Scenes for Neuromorphic Spike Camera [paper]

• Astromorphic Self-Repair of Neuromorphic Hardware Systems [paper]

ICML-2023

• Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks [paper]

• Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains [paper] [arxiv] [paper with code]

• Adaptive Smoothing Gradient Learning for Spiking Neural Networks [paper] [openreview]

• A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates [paper] [openreview]

ICLR-2023

• Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes [paper] [arxiv] [paper with code] [code] [openreview]

• Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles [paper] [arxiv] [paper with code] [openreview]

• Spiking Convolutional Neural Networks for Text Classification [paper] [openreview]

• Spikformer: When Spiking Neural Network Meets Transformer [paper] [arxiv] [paper with code] [code] [openreview]

IJCAI-2023

• Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Learnable Surrogate Gradient for Direct Training Spiking Neural Networks [paper]

• A Low Latency Adaptive Coding Spike Framework for Deep Reinforcement Learning [paper] [arxiv]

• Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks [paper]

• Spike Count Maximization for Neuromorphic Vision Recognition [paper]

• A New ANN-SNN Conversion Method with High Accuracy, Low Latency and Good Robustness [paper]

ICASSP-2023

• Joint ANN-SNN Co-training for Object Localization and Image Segmentation [paper]

• Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition [paper]

• Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching [paper]

• In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision [paper]

• Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons [paper]

• Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments [paper]

• Leveraging Sparsity with Spiking Recurrent Neural Networks for Energy-Efficient Keyword Spotting [paper]

IJCNN-2023

• Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction [paper]

• Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function [paper]

PAMI-2023

• Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN [paper] [code]

• Attention Spiking Neural Networks [paper] [code]

• CQ+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks [paper]

TNNLS-2023

• Attention-Based Deep Spiking Neural Networks for Temporal Credit Assignment Problems [paper]

• Effective Active Learning Method for Spiking Neural Networks [paper]

• Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey [paper]

Neural Networks-2023

• SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks [paper]

2022

CVPR-2022

• Spiking Transformers for Event-Based Single Object Tracking [paper] [paper with code]

• Brain-Inspired Multilayer Perceptron With Spiking Neurons [paper] [arxiv] [paper with code] [code]

• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [paper] [arxiv] [paper with code] [code]

• RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks [paper] [paper with code]

• Event-Based Video Reconstruction via Potential-Assisted Spiking Neural Network [paper] [arxiv] [paper with code] [code]

• Optical Flow Estimation for Spiking Camera [paper] [arxiv] [paper with code] [code]

ECCV-2022

• Spike Transformer: Monocular Depth Estimation for Spiking Camera [paper]

• Neuromorphic Data Augmentation for Training Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Reducing Information Loss for Spiking Neural Networks [paper] [arxiv]

• Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning [paper]

• Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Exploring Lottery Ticket Hypothesis in Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Neural Architecture Search for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Lottery Ticket Hypothesis for Spiking Neural Networks [paper]

NeurIPS-2022

• IM-Loss: Information Maximization Loss for Spiking Neural Networks [paper] [paper with code] [openreview]

• Biologically Inspired Dynamic Thresholds for Spiking Neural Networks [paper] [arxiv] [openreview]

• Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons [paper] [openreview]

• Learning Optical Flow from Continuous Spike Streams [paper] [openreview]

• Toward Robust Spiking Neural Network Against Adversarial Perturbation [paper] [arxiv] [paper with code] [openreview]

• Training Spiking Neural Networks with Local Tandem Learning [paper] [arxiv] [paper with code] [code] [openreview]

• Theoretically Provable Spiking Neural Networks [paper] [openreview]

• Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers [paper] [openreview]

• Biologically plausible solutions for spiking networks with efficient coding [paper] [arxiv] [paper with code] [openreview]

• Online Training Through Time for Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• Natural gradient enables fast sampling in spiking neural networks [paper] [openreview]

• Mesoscopic modeling of hidden spiking neurons [paper] [arxiv] [paper with code] [code] [openreview]

• SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training [paper] [openreview]

• Differentiable hierarchical and surrogate gradient search for spiking neural networks [paper] [openreview]

• LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout [paper] [openreview]

• Training Spiking Neural Networks with Event-driven Backpropagation [paper] [openreview]

• Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention [paper] [arxiv] [paper with code] [code] [openreview]

• GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• Temporal Effective Batch Normalization in Spiking Neural Networks [paper] [openreview]

• The computational and learning benefits of Daleian neural networks [paper]

• STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers [paper]

AAAI-2022

• Optimized Potential Initialization for Low-Latency Spiking Neural Networks [paper] [arxiv] [paper with code]

• Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning [paper]

• PrivateSNN: Privacy-Preserving Spiking Neural Networks [paper] [arxiv] [paper with code]

• SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks [paper]

• Fully Spiking Variational Autoencoder [paper] [arxiv] [paper with code] [code]

• Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning [paper] [arxiv] [paper with code] [code]

• Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation [paper] [code]

ICASSP-2022

• Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks [paper]

• Gradual Surrogate Gradient Learning in Deep Spiking Neural Networks [paper]

• T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events [paper]

• Modeling The Detection Capability Of High-Speed Spiking Cameras [paper]

• DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks [paper]

• Optimizing The Consumption Of Spiking Neural Networks With Activity Regularization [paper]

• Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? [paper]

• Motif-Topology and Reward-Learning Improved Spiking Neural Network for Efficient Multi-Sensory Integration [paper]

• Event-Based Multimodal Spiking Neural Network with Attention Mechanism [paper]

• A Hybrid Learning Framework for Deep Spiking Neural Networks with One-Spike Temporal Coding [paper]

• Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance [paper]

• A Time Encoding Approach to Training Spiking Neural Networks [paper]

ICML-2022

• State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks [paper]

• AutoSNN: Towards Energy-Efficient Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Scalable Spike-and-Slab [paper] [arxiv] [paper with code] [code]

• Neural Network Poisson Models for Behavioural and Neural Spike Train Data [paper]

IJCAI-2022

• Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes [paper] [arxiv] [paper with code] [code]

• Spiking Graph Convolutional Networks [paper] [arxiv] [paper with code] [code]

• Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion [paper] [code]

• Self-Supervised Mutual Learning for Dynamic Scene Reconstruction of Spiking Camera [paper]

• Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

ICLR-2022

• Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

• Spike-inspired rank coding for fast and accurate recurrent neural networks [paper] [arxiv] [paper with code] [code] [openreview]

• Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods [paper] [paper with code] [openreview]

• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting [paper] [arxiv] [paper with code] [code] [openreview]

IJCNN-2022

• Event-Driven Tactile Learning with Location Spiking Neurons [paper] [code]

• Spiking Approximations of the MaxPooling Operation in Deep SNNs [paper] [code]

• Spikemax: Spike-based Loss Methods for Classification [paper]

• Object Detection with Spiking Neural Networks on Automotive Event Data [paper] [code]

NEURAL COMPUTATION

• Training Deep Convolutional Spiking Neural Networks With Spike Probabilistic Global Pooling [paper]

Neural Networks-2022

• Modeling learnable electrical synapse for high precision spatio-temporal recognition [paper]

IEEE TCYB (IEEE Transactions on Cybernetics)

• Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning [paper]

2021

NeurIPS-2021

• Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks [paper] [arxiv] [paper with code] [openreview]

• Sparse Spiking Gradient Descent [paper] [arxiv] [paper with code] [code] [openreview]

• A universal probabilistic spike count model reveals ongoing modulation of neural variability [paper] [paper with code] [openreview]

• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [paper] [arxiv] [paper with code] [code] [openreview]

• Probabilistic Tensor Decomposition of Neural Population Spiking Activity [paper] [paper with code] [code] [openreview]

• Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck [paper] [arxiv] [paper with code] [openreview]

• Fitting summary statistics of neural data with a differentiable spiking network simulator [paper] [arxiv] [paper with code] [code] [openreview]

• Deep Residual Learning in Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Three-dimensional spike localization and improved motion correction for Neuropixels recordings [paper] [paper with code] [openreview]

• Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks [paper] [paper with code] [openreview]

CVPR-2021

• Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream [paper] [paper with code]

ICLR-2021

• Efficient Inference of Flexible Interaction in Spiking-neuron Networks [paper] [arxiv] [paper with code] [openreview]

• Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks [paper] [arxiv] [paper with code] [code] [openreview]

ICCV-2021

• HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training With Crafted Input Noise [paper] [arxiv] [paper with code] [code]

• DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks [paper] [arxiv] [paper with code]

• Super Resolve Dynamic Scene From Continuous Spike Streams [paper] [paper with code]

• Incorporating Learnable Membrane Time Constant To Enhance Learning of Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Temporal-Wise Attention Spiking Neural Networks for Event Streams Classification [paper] [arxiv] [paper with code]

ICML-2021

• Detection of Signal in the Spiked Rectangular Models [paper] [arxiv] [paper with code]

• A Differentiable Point Process with Its Application to Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration [paper] [arxiv] [paper with code] [code]

• Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks [paper] [arxiv] [paper with code]

IJCAI-2021

• Pruning of Deep Spiking Neural Networks through Gradient Rewiring [paper] [arxiv] [paper with code] [code]

• Event-based Action Recognition Using Motion Information and Spiking Neural Networks [paper]

• Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning [paper] [arxiv] [paper with code]

AAAI-2021

• Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information [paper]

• Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks [paper] [arxiv] [paper with code]

• Training Spiking Neural Networks with Accumulated Spiking Flow [paper]

• Near Lossless Transfer Learning for Spiking Neural Networks [paper]

• Going Deeper With Directly-Trained Larger Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance [paper] [arxiv] [paper with code] [code]

2020

NeurIPS-2020

• Rescuing neural spike train models from bad MLE [paper] [arxiv] [paper with code] [code]

• Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons [paper] [arxiv] [paper with code] [code]

• Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks [paper] [arxiv] [paper with code]

• Understanding spiking networks through convex optimization [paper] [paper with code] [code]

• Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Point process models for sequence detection in high-dimensional neural spike trains [paper] [arxiv] [paper with code] [code]

• Spike and slab variational Bayes for high dimensional logistic regression [paper] [arxiv] [paper with code]

• All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation [paper] [arxiv] [paper with code]

• Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors [paper] [arxiv] [paper with code]

• Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

CVPR-2020

• Retina-Like Visual Image Reconstruction via Spiking Neural Model [paper] [paper with code]

• RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network [paper] [arxiv] [paper with code] [code]

ICLR-2020

• Spike-based causal inference for weight alignment [paper] [arxiv] [paper with code] [code] [openreview]

• SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes [paper] [arxiv] [paper with code] [openreview]

• Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation [paper] [arxiv] [paper with code] [code] [openreview]

ECCV-2020

• Deep Spiking Neural Network: Energy Efficiency Through Time based Coding [paper]

• Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks [paper] [arxiv]

• Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations [paper] [arxiv]

ICML-2020

• Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations [paper] [arxiv] [paper with code]

IJCAI-2020

• LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition [paper]

• Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network [paper] [arxiv] [paper with code] [code]

AAAI-2020

• Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing [paper]

• Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks [paper] [arxiv] [paper with code]

• Biologically Plausible Sequence Learning with Spiking Neural Networks [paper] [arxiv] [paper with code]

• New Efficient Multi-Spike Learning for Fast Processing and Robust Learning [paper]

• Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection [paper] [arxiv] [paper with code]

2019

NeurIPS-2019

• The spiked matrix model with generative priors [paper] [arxiv] [paper with code] [code]

• Enabling hyperparameter optimization in sequential autoencoders for spiking neural data [paper] [arxiv] [paper with code] [code]

• Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference [paper] [arxiv] [paper with code] [code]

• Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models [paper] [paper with code] [code]

ICML-2019

• Weak Detection of Signal in the Spiked Wigner Model [paper]

• Bayesian Joint Spike-and-Slab Graphical Lasso [paper] [arxiv] [paper with code] [code]

• Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models [paper] [arxiv]

IJCAI-2019

• STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks [paper]

• Fast and Accurate Classification with a Multi-Spike Learning Algorithm for Spiking Neurons [paper]

AAAI-2019

• Direct Training for Spiking Neural Networks: Faster, Larger, Better [paper] [arxiv] [paper with code]

• TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding [paper]

• MPD-AL: An Efficient Membrane Potential Driven Aggregate-Label Learning Algorithm for Spiking Neurons [paper]

• Implementation of Boolean AND and OR Logic Gates with Biologically Reasonable Time Constants in Spiking Neural Networks [paper]

2018

NeurIPS-2018

• Gradient Descent for Spiking Neural Networks [paper] [arxiv]

• Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks [paper] [arxiv] [paper with code] [code]

• SLAYER: Spike Layer Error Reassignment in Time [paper] [arxiv] [paper with code] [code]

• Long short-term memory and Learning-to-learn in networks of spiking neurons [paper] [arxiv] [paper with code] [code]

• Temporal alignment and latent Gaussian process factor inference in population spike trains [paper] [paper with code]

ICLR-2018

• Temporally Efficient Deep Learning with Spikes [paper] [arxiv] [paper with code] [code] [openreview]

ICML-2018

• Non-linear motor control by local learning in spiking neural networks [paper] [arxiv] [paper with code] [code]

IJCAI-2018

• Jointly Learning Network Connections and Link Weights in Spiking Neural Networks [paper]

• CSNN: An Augmented Spiking based Framework with Perceptron-Inception [paper]

• Brain-inspired Balanced Tuning for Spiking Neural Networks [paper]

AAAI-2018

• A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks [paper]

• Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules [paper]

Starchart