{"id":13647815,"url":"https://github.com/XDUSPONGE/SNN_benchmark","last_synced_at":"2025-04-22T02:32:59.558Z","repository":{"id":37630301,"uuid":"263536830","full_name":"XDUSPONGE/SNN_benchmark","owner":"XDUSPONGE","description":null,"archived":false,"fork":false,"pushed_at":"2021-05-08T07:12:54.000Z","size":770,"stargazers_count":249,"open_issues_count":0,"forks_count":54,"subscribers_count":12,"default_branch":"master","last_synced_at":"2024-11-09T21:37:39.053Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/XDUSPONGE.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-05-13T05:46:57.000Z","updated_at":"2024-10-15T17:29:46.000Z","dependencies_parsed_at":"2022-07-18T01:16:53.376Z","dependency_job_id":null,"html_url":"https://github.com/XDUSPONGE/SNN_benchmark","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XDUSPONGE%2FSNN_benchmark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XDUSPONGE%2FSNN_benchmark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XDUSPONGE%2FSNN_benchmark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/XDUSPONGE%2FSNN_benchmark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/XDUSPONGE","download_url":"https://codeload.github.com/XDUSPONGE/SNN_benchmark/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250163795,"owners_count":21385317,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T01:03:47.373Z","updated_at":"2025-04-22T02:32:59.320Z","avatar_url":"https://github.com/XDUSPONGE.png","language":null,"funding_links":[],"categories":["Others","Review"],"sub_categories":[],"readme":"# Spiking Neural Network Paper List\n## Framework\n* BindsNet[[code](https://github.com/BindsNET/bindsnet)] \u003cbr /\u003e\n* Brain2[[code](https://github.com/brian-team/brian2)] \u003cbr /\u003e\n* SpykeTorch[[code](https://github.com/miladmozafari/SpykeTorch)] \u003cbr /\u003e\n* Norse[[code](https://github.com/norse/norse)] \u003cbr /\u003e\n* SpikingJelly[[code](https://github.com/fangwei123456/SpikingFlow)] \u003cbr /\u003e\n* Nengo[[code](https://github.com/nengo/nengo)] \u003cbr /\u003e\n* PySNN[[code](https://github.com/BasBuller/PySNN)] \u003cbr /\u003e\n* SNN_toolbox[[code](https://github.com/NeuromorphicProcessorProject/snn_toolbox)] \u003cbr /\u003e\n## SNN Adversarial Robustness\n* Saima Sharmin, Nitin Rathi, Priyadarshini Panda, Kaushik Roy ***ECCV 2020***\u003cbr /\u003e\n\" Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations\"\n[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6748_ECCV_2020_paper.php)]\n [[code](https://github.com/ssharmin/spikingNN-adversarial-attack)]\n* Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, Kaushik Roy ***IJCNN 2019*** \u003cbr /\u003e\n\"A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8851732)]\n* Akhilesh Jaiswal, Amogh Agrawal, Indranil Chakraborty, Deboleena Roy, Kaushik Roy ***IJCNN 2019***\u003cbr /\u003e\n\"On Robustness of Spin-Orbit-Torque Based Stochastic Sigmoid Neurons for Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8851780)]\n * Xueyuan She, Yun Long, Saibal Mukhopadhyay ***IJCNN 2019***\u003cbr /\u003e\n\"Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity\".\n [[paper](https://ieeexplore.ieee.org/document/8851825)]\n## Other Application\n* Allan Mancoo, Sander W. Keemink, Christian K. Machens  ***NIPS 2020***\u003cbr /\u003e\n\"Understanding spiking networks through convex optimization\"\n [[paper](https://proceedings.neurips.cc/paper/2020/file/64714a86909d401f8feb83e8c2d94b23-Paper.pdf)]\n * Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon ***AAAI 2020***\u003cbr /\u003e\n**Spiking-YOLO:** \"Spiking Neural Network for Energy-Efficient Object Detection\"\n [[paper](https://arxiv.org/pdf/1903.06530.pdf)]\n * Biswadeep Chakraborty, Xueyuan She \u003cbr /\u003e\n\"A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection\"\n [[paper](https://arxiv.org/abs/2104.10719)]\n## Papers\nFor the Spiking Neural Network studies, it can be roughly divided into three categories\n* The Conversion Method (Converting a well-trained ann to snn)\n* SNN trained with BP\n* SNN trained with Biological Plasticity Rules (STDP, Hebbian,etc)\n### Conversion Based Methods\n* Weihao Tan, Devdhar Patel, Robert Kozma ***AAAI 2021***\u003cbr /\u003e\n\"Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks\"\n [[paper](https://arxiv.org/pdf/2009.14456.pdf)]\n* Zhanglu Yan, Jun Zhou, Weng-Fai Wong ***AAAI 2021***\u003cbr /\u003e\n\"Near Lossless Transfer Learning for Spiking Neural Networks\"\n [[paper](https://www.comp.nus.edu.sg/~wongwf/papers/AAAI-2021.pdf)]\n* Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy ***CVPR 2020***\u003cbr /\u003e\n\"RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network\"\n[[paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Han_RMP-SNN_Residual_Membrane_Potential_Neuron_for_Enabling_Deeper_High-Accuracy_and_CVPR_2020_paper.html)]\n* Bing Han, Kaushik Roy ***ECCV 2020*** \u003cbr /\u003e\n\"Deep Spiking Neural Network: Energy Efficiency Through Time based Coding\"\n[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1103_ECCV_2020_paper.php)]\n * Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy ***ICLR 2020***\u003cbr /\u003e\n\"Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation\".\n [[paper](https://arxiv.org/pdf/2005.01807.pdf)]\n [[code](https://github.com/nitin-rathi/hybrid-snn-conversion.git)]\n * Lei Zhang, Shengyuan Zhou, Tian Zhi, Zidong Du, Yunji Chen ***AAAI 2019***\u003cbr /\u003e\n**TDSNN** \"DFrom Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding\".\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3931)]\n * Ruizhi Chen, Hong Ma, Shaolin Xie, Peng Guo, Pin Li, Donglin Wang ***IJCNN 2018***\u003cbr /\u003e\n\"Fast and Efficient Deep Sparse Multi-Strength Spiking Neural Networks with Dynamic Pruning\".\n  * Jingling Li, Weitai Hu, Ye Yuan, Hong Huo, Tao Fang ***ICONIP 2017***\u003cbr /\u003e\n\"Bio-Inspired Deep Spiking Neural Network for Image Classification\".\n [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70096-0_31)]\n [[paper](https://ieeexplore.ieee.org/document/8489339)]\n\n### SNN trained with BP\n* Shibo Zhou, Xiaohua LI, Ying Chen, Sanjeev T. Chandrasekaran, Arindam Sanyal ***AAAI 2021***\u003cbr /\u003e\n\"Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance\"\n [[paper](https://arxiv.org/pdf/1909.10837.pdf)]\n  [[code](https://github.com/zbs881314/Temporal-Coded-Deep-SNN)]\n* Hao Wu, Yueyi Zhang, ... ***AAAI 2021***\u003cbr /\u003e\n\"Training Spiking Neural Networks with Accumulated Spiking Flow\"\n [[paper](https://www.aaai.org/AAAI21Papers/AAAI-4138.WuHao.pdf)]\n* Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, Guoqi Li ***AAAI 2021***\u003cbr /\u003e\n\"Going Deeper With Directly-Trained Larger Spiking Neural Networks\"\n [[paper](https://arxiv.org/abs/2011.05280)]\n * Wenrui Zhang, Peng Li ***NIPS 2020***\u003cbr /\u003e\n\"Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks\"\n [[paper](https://arxiv.org/abs/2002.10085)]\n [[code](https://github.com/stonezwr/TSSL-BP)]\n* Jinseok Kim, Kyungsu Kim, Jae-Joon Kim ***NIPS 2020***\u003cbr /\u003e\n\"Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks\"\n [[paper](https://papers.nips.cc/paper/2020/file/e2e5096d574976e8f115a8f1e0ffb52b-Paper.pdf)]\n [[code](https://github.com/KyungsuKim42/ANTLR)]\n * Qianyi Li, Cengiz Pehlevan ***NIPS 2020***\u003cbr /\u003e\n\"Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons\"\n [[paper](https://arxiv.org/abs/2006.08115)]\n* Haowen Fang, Amar Shrestha, Ziyi Zhao, Qinru Qiu ***IJCAI 2020***\u003cbr /\u003e\n\"Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network\"\n [[paper](https://www.ijcai.org/Proceedings/2020/0388.pdf)]\n [[code](https://github.com/Snow-Crash/snn-iir)]\n * Xiang Cheng, Yunzhe Hao, Jiaming Xu, Bo Xu ***IJCAI 2020***\u003cbr /\u003e\n**LISNN**: \"Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition\"\n [[paper](https://www.ijcai.org/Proceedings/2020/0211.pdf)]\n * Johannes C. Thiele, Olivier Bichler, Antoine Dupret ***ICLR 2020***\u003cbr /\u003e\n\"SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes\".\n [[paper](https://arxiv.org/pdf/1906.00851.pdf)]\n * Jordan Guerguiev, Konrad P. Körding, Blake A. Richards ***ICLR 2020*** \u003cbr /\u003e\n\"Spike-based causal inference for weight alignment\".\n [[paper](https://arxiv.org/pdf/1910.01689.pdf)]\n [[code](https://anonfile.com/51V8Ge66n3/Code_zip)]\n  * Kian Hamedani, Lingjia Liu, Shiya Liu, Haibo He, Yang Yi ***AAAI 2020***\u003cbr /\u003e\n\"Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing\"\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5484)]\n * Wenrui Zhang, Peng Li ***NIPS 2019***\u003cbr /\u003e\n\"Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks\".\n [[paper](http://papers.nips.cc/paper/8995-spike-train-level-backpropagation-for-training-deep-recurrent-spiking-neural-networks)]\n* Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, Luping Shi ***AAAI 2019***\u003cbr /\u003e\n\"Direct Training for Spiking Neural Networks: Faster, Larger, Better\".\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3929)]\n [[code]( https://github.com/yjwu17/BP-for-SpikingNN)]\n * Malu Zhang, Jibin Wu, Yansong Chua, Xiaoling Luo, Zihan Pan, Dan Liu, Haizhou Li ***AAAI 2019***\u003cbr /\u003e\n**MPD-AL** \"An Efficient Membrane Potential Driven Aggregate-Label Learning Algorithm for Spiking Neurons\".\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3932)]\n* Cengiz Pehlevan ***ICASSP 2019***\u003cbr /\u003e\n\"A Spiking Neural Network with Local Learning Rules Derived from Nonnegative Similarity Matching\".\n [[paper](https://ieeexplore.ieee.org/document/8682290)]\n* Megumi Ito, Malte J. Rasch, Masatoshi Ishii, Atsuya Okazaki, SangBum Kim, Junka Okazawa, Akiyo Nomura, Kohji Hosokawa, Wilfried Haensch***ICONIP 2019*** \u003cbr /\u003e\n\"Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation\".\n [[paper](https://link.springer.com/chapter/10.1007%2F978-3-030-36718-3_16)]\n * Johannes C. Thiele, Olivier Bichler, Antoine Dupret, Sergio Solinas, Giacomo Indiveri ***IJCNN 2019***\u003cbr /\u003e\n\"A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation\".\n [[paper](https://ieeexplore.ieee.org/document/8852360)]\n  * Thomas Miconi, Jeff Clune, Kenneth O. Stanley ***ICML 2018***\u003cbr /\u003e\n\"Differentiable plasticity: training plastic neural networks with backpropagation\".\n [[paper](https://arxiv.org/abs/1804.02464)]\n  [[code]( https://github.com/uber-research/differentiable-plasticity)]\n * \tDongsung Huh, Terrence J. Sejnowski ***NIPS 2018***\u003cbr /\u003e\n\"Gradient Descent for Spiking Neural Networks\".\n [[paper](http://papers.nips.cc/paper/7417-gradient-descent-for-spiking-neural-networks)]\n* Yingyezhe Jin, Wenrui Zhang, Peng Li ***NIPS 2018***\u003cbr /\u003e\n\"Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks\".\n [[paper](http://papers.nips.cc/paper/7932-hybrid-macromicro-level-backpropagation-for-training-deep-spiking-neural-networks)]\n \n### SNN trained with Biological Plasticity Rules (STDP, Hebbian,etc)\n* Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, Kaushik Roy ***ECCV 2020***\u003cbr /\u003e\n\"Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks\"\n[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6736_ECCV_2020_paper.php)]\n[[code](https://github.com/chan8972/Spike-FlowNet)]\n* Lin Zhu, Siwei Dong, Jianing Li, Tiejun Huang, Yonghong Tian ***CVPR 2020***\u003cbr /\u003e\n\"Retina-Like Visual Image Reconstruction via Spiking Neural Model\"\n[[paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html)]\n* Qianhui Liu, Haibo Ruan, Dong Xing, Huajin Tang, Gang Pan ***AAAI 2020***\u003cbr /\u003e\n\"Effective AER Object Classification Using Segmented Probability-Maximization\nLearning in Spiking Neural Networks\" AAAI (2020 **Oral**).\n [[paper](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuQ.6583.pdf)]\n* Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin ***AAAI 2020*** \u003cbr /\u003e\n\"Biologically Plausible Sequence Learning with Spiking Neural Networks\"\n [[paper](https://arxiv.org/abs/1911.10943)]\n [[code](https://github.com/owen94/MPNets)]\n * Shenglan Li, Qiang Yu ***AAAI 2020***\u003cbr /\u003e\n\"New Efficient Multi-Spike Learning for Fast Processing and Robust Learning\"\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5896)]\n * Pengjie Gu, Rong Xiao, Gang Pan, Huajin Tang ***IJCAI 2019***\u003cbr /\u003e\n**STCA:** \"STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks\".\n [[paper](https://www.ijcai.org/proceedings/2019/0189.pdf)]\n [[code](https://github.com/Squirtle-gpj/STCA-DSNN)]\n * Rong Xiao, Qiang Yu, Rui Yan, Huajin Tang ***IJCAI 2019***\u003cbr /\u003e\n\"Fast and Accurate Classification with a Multi-Spike Learning Algorithm for Spiking Neurons\".\n [[paper](https://www.ijcai.org/Proceedings/2019/0200.pdf)]\n  * Lakshay Sahni, Debasrita Chakraborty, Ashish Ghosh ***AAAI 2019***\u003cbr /\u003e\n\"Implementation of Boolean AND and OR Logic Gates with Biologically Reasonable Time Constants in Spiking Neural Networks\".\n [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5147)]\n  * Robert Luke, David McAlpine ***ICASSP 2019***\u003cbr /\u003e\n\"A Spiking Neural Network Approach to Auditory Source Lateralisation\".\n [[paper](https://ieeexplore.ieee.org/document/8683767)]\n  * Hui Yan, Xinle Liu, Hong Huo, Tao Fang ***ICONIP 2019***\u003cbr /\u003e\n\"Mechanisms of Reward-Modulated STDP and Winner-Take-All in Bayesian Spiking Decision-Making Circuit\".\n [[paper](https://link.springer.com/chapter/10.1007%2F978-3-030-36718-3_14)]\n  * Yanli Yao, Qiang Yu, Longbiao Wang, Jianwu Dang ***IJCNN 2019***\u003cbr /\u003e\n\"A Spiking Neural Network with Distributed Keypoint Encoding for Robust Sound Recognition\".\n [[paper](https://ieeexplore.ieee.org/document/8852166)]\n  * Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet ***IJCNN 2019*** \u003cbr /\u003e\n\"Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP\".\n [[paper](https://ieeexplore.ieee.org/document/8852346)]\n * Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li ***IJCNN 2019***\u003cbr /\u003e\n\"Deep Spiking Neural Network with Spike Count based Learning Rule\".\n [[paper](https://ieeexplore.ieee.org/document/8852380)]\n  * Maximilian P. R. Löhr, Daniel Schmid, Heiko Neumann ***IJCNN 2019***\u003cbr /\u003e\n\"Motion Integration and Disambiguation by Spiking V1-MT-MSTl Feedforward-Feedback Interaction\".\n [[paper](https://ieeexplore.ieee.org/document/8852029)]\n   * Esma Mansouri-Benssassi, Juan Ye ***IJCNN 2019***\u003cbr /\u003e\n\"Speech Emotion Recognition With Early Visual Cross-modal Enhancement Using Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8852473)]\n * \tMikhail Kiselev, Andrey Lavrentyev ***IJCNN 2019***\u003cbr /\u003e\n\"A Preprocessing Layer in Spiking Neural Networks - Structure, Parameters, Performance Criteria\".\n [[paper](https://ieeexplore.ieee.org/document/8851848)]\n *Won-Mook Kang, Chul-Heung Kim, Soochang Lee, Sung Yun Woo, Jong-Ho Bae, Byung-Gook Park, Jong-Ho Lee ***IJCNN 2019***\u003cbr /\u003e\n\"A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices\".\n [[paper](https://ieeexplore.ieee.org/document/8851744)]\n  * Lyes Khacef, Benoît Miramond, Diego Barrientos, Andres Upegui ***IJCNN 2019***\u003cbr /\u003e\n\"Self-organizing neurons: toward brain-inspired unsupervised learning\".\n [[paper](https://ieeexplore.ieee.org/document/8852098)]\n * \tPeter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling ***ICLR 2018***\u003cbr /\u003e\n\"Temporally Efficient Deep Learning with Spikes\".\n [[paper](https://openreview.net/forum?id=HkZy-bW0-)]\n * \tAditya Gilra, Wulfram Gerstner ***ICML 2018***\u003cbr /\u003e\n\"Non-Linear Motor Control by Local Learning in Spiking Neural Networks\".\n [[paper](http://proceedings.mlr.press/v80/gilra18a.html)]\n *Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert A. Legenstein, Wolfgang Maass ***NIPS 2018***\u003cbr /\u003e\n\"Long short-term memory and Learning-to-learn in networks of spiking neurons\".\n [[paper](http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons)]\n * Sumit Bam Shrestha, Garrick Orchard ***NIPS 2018***\u003cbr /\u003e\n**SLAYER** \"Spike Layer Error Reassignment in Time\".\n [[paper](http://papers.nips.cc/paper/7415-slayer-spike-layer-error-reassignment-in-time)]\n [[code]( https://github.com/bamsumit/slayerPytorch)]\n * Yu Qi, Jiangrong Shen, Yueming Wang, Huajin Tang, Hang Yu, Zhaohui Wu, Gang Pan ***IJCAI 2018***\u003cbr /\u003e\nJointly Learning Network Connections and Link Weights in Spiking Neural Networks\".\n [[paper](https://www.ijcai.org/Proceedings/2018/221)]\n* Qi Xu, Yu Qi, Hang Yu, Jiangrong Shen, Huajin Tang, Gang Pan ***IJCAI 2018***\u003cbr /\u003e\n**CSNN** \"An Augmented Spiking based Framework with Perceptron-Inception\".\n [[paper](https://www.ijcai.org/Proceedings/2018/228)]\n * Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu ***IJCAI 2018***\u003cbr /\u003e\n**VPSNN** Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu.\n [[paper](https://www.ijcai.org/Proceedings/2018/229)]\n  * Alireza Alemi, Christian K. Machens, Sophie Denève, Jean-Jacques E. Slotine ***AAAI 2018***\u003cbr /\u003e\n\"Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules\".\n [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17438)]\n * Tielin Zhang, Yi Zeng, Dongcheng Zhao, Mengting Shi ***AAAI 2018***\u003cbr /\u003e\n\"A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks\".\n [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16105)]\n  * Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran ***ICASSP 2018***\u003cbr /\u003e\n\"Training Probabilistic Spiking Neural Networks with First- To-Spike Decoding\".\n [[paper](https://arxiv.org/pdf/1710.10704.pdf)]\n   * Qiang Yu, Longbiao Wang, Jianwu Dang ***ICONIP 2018***\u003cbr /\u003e\n\"Efficient Multi-spike Learning with Tempotron-Like LTP and PSD-Like LTD\".\n [[paper](https://doi.org/10.1007/978-3-030-04167-0_49)]\n  * Jiaxing Liu, Guoping Zhao ***IJCNN 2018***\u003cbr /\u003e\n\"A bio-inspired SOSNN model for object recognition\".\n [[paper](https://ieeexplore.ieee.org/document/8489076/)]\n   * Amirhossein Tavanaei, Zachary Kirby, Anthony S. Maida ***IJCNN 2018***\u003cbr /\u003e\n\"Training Spiking ConvNets by STDP and Gradient Descent\".\n [[paper](https://ieeexplore.ieee.org/document/8489104)]\n   * Yu Miao, Huajin Tang, Gang Pan ***IJCNN 2018***\u003cbr /\u003e\n\"A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489175)]\n * Timoleon Moraitis, Abu Sebastian, Evangelos Eleftheriou ***IJCNN 2018***\u003cbr /\u003e\n\"Spiking Neural Networks Enable Two-Dimensional Neurons and Unsupervised Multi-Timescale Learning\".\n [[paper](https://ieeexplore.ieee.org/document/8489218)]\n  * Sam Slade, Li Zhang ***IJCNN 2018***\u003cbr /\u003e\n\"Topological Evolution of Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489375)]\n  *\tRuizhi Chen, Hong Ma, Peng Guo, Shaolin Xie, Pin Li, Donglin Wang ***IJCNN 2018***\u003cbr /\u003e\n\"Low Latency Spiking ConvNets with Restricted Output Training and False Spike Inhibition\".\n [[paper](https://ieeexplore.ieee.org/document/8489400)]\n * Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet ***IJCNN 2018***\u003cbr /\u003e\n\"Mastering the Output Frequency in Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489410)]\n  *\tDaqi Liu, Shigang Yue ***IJCNN 2018***\u003cbr /\u003e\n\"Video-Based Disguise Face Recognition Based on Deep Spiking Neural Network\".\n [[paper](https://ieeexplore.ieee.org/document/8489476)]\n  * Johannes C. Thiele, Olivier Bichler, Antoine Dupret ***IJCNN 2018***\u003cbr /\u003e\n\"A Timescale Invariant STDP-Based Spiking Deep Network for Unsupervised Online Feature Extraction from Event-Based Sensor Data\".\n [[paper](https://ieeexplore.ieee.org/document/8489666)]\n  * Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma ***IJCNN 2018***\u003cbr /\u003e\n\"Unsupervised Learning with Self-Organizing Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489673)]\n  * Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó ***IJCNN 2018***\u003cbr /\u003e\n\"STDP Learning of Image Patches with Convolutional Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489684)]\n   *\tJibin Wu, Yansong Chua, Haizhou Li ***IJCNN 2018***\u003cbr /\u003e\n\"A Biologically Plausible Speech Recognition Framework Based on Spiking Neural Networks\".\n [[paper](https://ieeexplore.ieee.org/document/8489535)]\n * Antonio Jimeno-Yepes, Jianbin Tang, Benjamin Scott Mashford ***IJCAI 2017***\u003cbr /\u003e\n\"Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips\".\n [[paper](https://www.ijcai.org/Proceedings/2017/274)]\n  * Zhanhao Hu, Tao Wang, Xiaolin Hu ***ICONIP 2017***\u003cbr /\u003e\n\"An STDP-Based Supervised Learning Algorithm for Spiking Neural Network\".\n [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70096-0_10)]\n   * Lin Zuo, Shan Chen, Hong Qu, Malu Zhang ***ICONIP 2017***\u003cbr /\u003e\n\"A Fast Precise-Spike and Weight-Comparison Based Learning Approach for Evolving Spiking Neural Networks\".\n [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70090-8_81)]\n   * Amirhossein Tavanaei, Anthony S. Maida ***IJCNN 2017***\u003cbr /\u003e\n\"Multi-layer unsupervised learning in a spiking convolutional neural network\".\n [[paper](https://ieeexplore.ieee.org/document/7966099)]\n   * Takashi Matsubara ***IJCNN 2017***\u003cbr /\u003e\n\"Spike timing-dependent conduction delay learning model classifying spatio-temporal spike patterns\".\n [[paper](https://ieeexplore.ieee.org/document/7966073)]\n   * Laxmi R. Iyer, Arindam Basu ***IJCNN 2017***\u003cbr /\u003e\n\"Unsupervised learning of event-based image recordings using spike-timing-dependent plasticity\".\n [[paper](https://ieeexplore.ieee.org/document/7966074)]\n * \tGopalakrishnan Srinivasan, Sourjya Roy, Vijay Raghunathan, Kaushik Roy ***IJCNN 2017***\u003cbr /\u003e\n\"Spike timing dependent plasticity based enhanced self-learning for efficient pattern recognition in spiking neural networks\".\n [[paper](https://ieeexplore.ieee.org/document/7966075)]\n * Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, Qinru Qiu ***IJCNN 2017***\u003cbr /\u003e\n\"Stable spike-timing dependent plasticity rule for multilayer unsupervised and supervised learning\".\n [[paper](https://ieeexplore.ieee.org/document/7966096)]\n  * Timoleon Moraitis, Abu Sebastian, Irem Boybat, Manuel Le Gallo, Tomas Tuma, Evangelos Eleftheriou ***IJCNN 2017***\u003cbr /\u003e\n\"Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes\".\n [[paper](https://ieeexplore.ieee.org/document/7966072)]\n   * Yingyezhe Jin, Peng Li ***IJCNN 2017***\u003cbr /\u003e\n\"Calcium-modulated supervised spike-timing-dependent plasticity for readout training and sparsification of the liquid state machine\".\n [[paper](https://ieeexplore.ieee.org/document/7966097)]\n * \tAmirali Amirsoleimani, Majid Ahmadi, Arash Ahmadi ***IJCNN 2017***\u003cbr /\u003e\n\"STDP-based unsupervised learning of memristive spiking neural network by Morris-Lecar model\".\n [[paper](https://ieeexplore.ieee.org/document/7966284)]","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXDUSPONGE%2FSNN_benchmark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FXDUSPONGE%2FSNN_benchmark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXDUSPONGE%2FSNN_benchmark/lists"}