https://github.com/XDUSPONGE/SNN_benchmark

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URL: https://github.com/XDUSPONGE/SNN_benchmark
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Created: 2020-05-13T05:46:57.000Z (over 5 years ago)
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- Readme: README.md
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README

          # Spiking Neural Network Paper List

## Framework

* BindsNet[[code](https://github.com/BindsNET/bindsnet)] 


* Brain2[[code](https://github.com/brian-team/brian2)] 


* SpykeTorch[[code](https://github.com/miladmozafari/SpykeTorch)] 


* Norse[[code](https://github.com/norse/norse)] 


* SpikingJelly[[code](https://github.com/fangwei123456/SpikingFlow)] 


* Nengo[[code](https://github.com/nengo/nengo)] 


* PySNN[[code](https://github.com/BasBuller/PySNN)] 


* SNN_toolbox[[code](https://github.com/NeuromorphicProcessorProject/snn_toolbox)] 


## SNN Adversarial Robustness

* Saima Sharmin, Nitin Rathi, Priyadarshini Panda, Kaushik Roy ***ECCV 2020***


" Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations"

[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6748_ECCV_2020_paper.php)]

 [[code](https://github.com/ssharmin/spikingNN-adversarial-attack)]

* Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, Kaushik Roy ***IJCNN 2019*** 


"A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8851732)]

* Akhilesh Jaiswal, Amogh Agrawal, Indranil Chakraborty, Deboleena Roy, Kaushik Roy ***IJCNN 2019***


"On Robustness of Spin-Orbit-Torque Based Stochastic Sigmoid Neurons for Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8851780)]

 * Xueyuan She, Yun Long, Saibal Mukhopadhyay ***IJCNN 2019***


"Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity".

 [[paper](https://ieeexplore.ieee.org/document/8851825)]

## Other Application

* Allan Mancoo, Sander W. Keemink, Christian K. Machens  ***NIPS 2020***


"Understanding spiking networks through convex optimization"

 [[paper](https://proceedings.neurips.cc/paper/2020/file/64714a86909d401f8feb83e8c2d94b23-Paper.pdf)]

 * Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon ***AAAI 2020***


**Spiking-YOLO:** "Spiking Neural Network for Energy-Efficient Object Detection"

 [[paper](https://arxiv.org/pdf/1903.06530.pdf)]

 * Biswadeep Chakraborty, Xueyuan She 


"A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection"

 [[paper](https://arxiv.org/abs/2104.10719)]

## Papers

For the Spiking Neural Network studies, it can be roughly divided into three categories

* The Conversion Method (Converting a well-trained ann to snn)

* SNN trained with BP

* SNN trained with Biological Plasticity Rules (STDP, Hebbian,etc)

### Conversion Based Methods

* Weihao Tan, Devdhar Patel, Robert Kozma ***AAAI 2021***


"Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks"

 [[paper](https://arxiv.org/pdf/2009.14456.pdf)]

* Zhanglu Yan, Jun Zhou, Weng-Fai Wong ***AAAI 2021***


"Near Lossless Transfer Learning for Spiking Neural Networks"

 [[paper](https://www.comp.nus.edu.sg/~wongwf/papers/AAAI-2021.pdf)]

* Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy ***CVPR 2020***


"RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network"

[[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)]

* Bing Han, Kaushik Roy ***ECCV 2020*** 


"Deep Spiking Neural Network: Energy Efficiency Through Time based Coding"

[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1103_ECCV_2020_paper.php)]

 * Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy ***ICLR 2020***


"Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation".

 [[paper](https://arxiv.org/pdf/2005.01807.pdf)]

 [[code](https://github.com/nitin-rathi/hybrid-snn-conversion.git)]

 * Lei Zhang, Shengyuan Zhou, Tian Zhi, Zidong Du, Yunji Chen ***AAAI 2019***


**TDSNN** "DFrom Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding".

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3931)]

 * Ruizhi Chen, Hong Ma, Shaolin Xie, Peng Guo, Pin Li, Donglin Wang ***IJCNN 2018***


"Fast and Efficient Deep Sparse Multi-Strength Spiking Neural Networks with Dynamic Pruning".

  * Jingling Li, Weitai Hu, Ye Yuan, Hong Huo, Tao Fang ***ICONIP 2017***


"Bio-Inspired Deep Spiking Neural Network for Image Classification".

 [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70096-0_31)]

 [[paper](https://ieeexplore.ieee.org/document/8489339)]

### SNN trained with BP

* Shibo Zhou, Xiaohua LI, Ying Chen, Sanjeev T. Chandrasekaran, Arindam Sanyal ***AAAI 2021***


"Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance"

 [[paper](https://arxiv.org/pdf/1909.10837.pdf)]

  [[code](https://github.com/zbs881314/Temporal-Coded-Deep-SNN)]

* Hao Wu, Yueyi Zhang, ... ***AAAI 2021***


"Training Spiking Neural Networks with Accumulated Spiking Flow"

 [[paper](https://www.aaai.org/AAAI21Papers/AAAI-4138.WuHao.pdf)]

* Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, Guoqi Li ***AAAI 2021***


"Going Deeper With Directly-Trained Larger Spiking Neural Networks"

 [[paper](https://arxiv.org/abs/2011.05280)]

 * Wenrui Zhang, Peng Li ***NIPS 2020***


"Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks"

 [[paper](https://arxiv.org/abs/2002.10085)]

 [[code](https://github.com/stonezwr/TSSL-BP)]

* Jinseok Kim, Kyungsu Kim, Jae-Joon Kim ***NIPS 2020***


"Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks"

 [[paper](https://papers.nips.cc/paper/2020/file/e2e5096d574976e8f115a8f1e0ffb52b-Paper.pdf)]

 [[code](https://github.com/KyungsuKim42/ANTLR)]

 * Qianyi Li, Cengiz Pehlevan ***NIPS 2020***


"Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons"

 [[paper](https://arxiv.org/abs/2006.08115)]

* Haowen Fang, Amar Shrestha, Ziyi Zhao, Qinru Qiu ***IJCAI 2020***


"Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network"

 [[paper](https://www.ijcai.org/Proceedings/2020/0388.pdf)]

 [[code](https://github.com/Snow-Crash/snn-iir)]

 * Xiang Cheng, Yunzhe Hao, Jiaming Xu, Bo Xu ***IJCAI 2020***


**LISNN**: "Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition"

 [[paper](https://www.ijcai.org/Proceedings/2020/0211.pdf)]

 * Johannes C. Thiele, Olivier Bichler, Antoine Dupret ***ICLR 2020***


"SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes".

 [[paper](https://arxiv.org/pdf/1906.00851.pdf)]

 * Jordan Guerguiev, Konrad P. Körding, Blake A. Richards ***ICLR 2020*** 


"Spike-based causal inference for weight alignment".

 [[paper](https://arxiv.org/pdf/1910.01689.pdf)]

 [[code](https://anonfile.com/51V8Ge66n3/Code_zip)]

  * Kian Hamedani, Lingjia Liu, Shiya Liu, Haibo He, Yang Yi ***AAAI 2020***


"Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing"

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5484)]

 * Wenrui Zhang, Peng Li ***NIPS 2019***


"Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks".

 [[paper](http://papers.nips.cc/paper/8995-spike-train-level-backpropagation-for-training-deep-recurrent-spiking-neural-networks)]

* Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, Luping Shi ***AAAI 2019***


"Direct Training for Spiking Neural Networks: Faster, Larger, Better".

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3929)]

 [[code]( https://github.com/yjwu17/BP-for-SpikingNN)]

 * Malu Zhang, Jibin Wu, Yansong Chua, Xiaoling Luo, Zihan Pan, Dan Liu, Haizhou Li ***AAAI 2019***


**MPD-AL** "An Efficient Membrane Potential Driven Aggregate-Label Learning Algorithm for Spiking Neurons".

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/3932)]

* Cengiz Pehlevan ***ICASSP 2019***


"A Spiking Neural Network with Local Learning Rules Derived from Nonnegative Similarity Matching".

 [[paper](https://ieeexplore.ieee.org/document/8682290)]

* Megumi Ito, Malte J. Rasch, Masatoshi Ishii, Atsuya Okazaki, SangBum Kim, Junka Okazawa, Akiyo Nomura, Kohji Hosokawa, Wilfried Haensch***ICONIP 2019*** 


"Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation".

 [[paper](https://link.springer.com/chapter/10.1007%2F978-3-030-36718-3_16)]

 * Johannes C. Thiele, Olivier Bichler, Antoine Dupret, Sergio Solinas, Giacomo Indiveri ***IJCNN 2019***


"A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation".

 [[paper](https://ieeexplore.ieee.org/document/8852360)]

  * Thomas Miconi, Jeff Clune, Kenneth O. Stanley ***ICML 2018***


"Differentiable plasticity: training plastic neural networks with backpropagation".

 [[paper](https://arxiv.org/abs/1804.02464)]

  [[code]( https://github.com/uber-research/differentiable-plasticity)]

 * 	Dongsung Huh, Terrence J. Sejnowski ***NIPS 2018***


"Gradient Descent for Spiking Neural Networks".

 [[paper](http://papers.nips.cc/paper/7417-gradient-descent-for-spiking-neural-networks)]

* Yingyezhe Jin, Wenrui Zhang, Peng Li ***NIPS 2018***


"Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks".

 [[paper](http://papers.nips.cc/paper/7932-hybrid-macromicro-level-backpropagation-for-training-deep-spiking-neural-networks)]

 

### SNN trained with Biological Plasticity Rules (STDP, Hebbian,etc)

* Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, Kaushik Roy ***ECCV 2020***


"Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks"

[[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6736_ECCV_2020_paper.php)]

[[code](https://github.com/chan8972/Spike-FlowNet)]

* Lin Zhu, Siwei Dong, Jianing Li, Tiejun Huang, Yonghong Tian ***CVPR 2020***


"Retina-Like Visual Image Reconstruction via Spiking Neural Model"

[[paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html)]

* Qianhui Liu, Haibo Ruan, Dong Xing, Huajin Tang, Gang Pan ***AAAI 2020***


"Effective AER Object Classification Using Segmented Probability-Maximization

Learning in Spiking Neural Networks" AAAI (2020 **Oral**).

 [[paper](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuQ.6583.pdf)]

* Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin ***AAAI 2020*** 


"Biologically Plausible Sequence Learning with Spiking Neural Networks"

 [[paper](https://arxiv.org/abs/1911.10943)]

 [[code](https://github.com/owen94/MPNets)]

 * Shenglan Li, Qiang Yu ***AAAI 2020***


"New Efficient Multi-Spike Learning for Fast Processing and Robust Learning"

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5896)]

 * Pengjie Gu, Rong Xiao, Gang Pan, Huajin Tang ***IJCAI 2019***


**STCA:** "STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks".

 [[paper](https://www.ijcai.org/proceedings/2019/0189.pdf)]

 [[code](https://github.com/Squirtle-gpj/STCA-DSNN)]

 * Rong Xiao, Qiang Yu, Rui Yan, Huajin Tang ***IJCAI 2019***


"Fast and Accurate Classification with a Multi-Spike Learning Algorithm for Spiking Neurons".

 [[paper](https://www.ijcai.org/Proceedings/2019/0200.pdf)]

  * Lakshay Sahni, Debasrita Chakraborty, Ashish Ghosh ***AAAI 2019***


"Implementation of Boolean AND and OR Logic Gates with Biologically Reasonable Time Constants in Spiking Neural Networks".

 [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/5147)]

  * Robert Luke, David McAlpine ***ICASSP 2019***


"A Spiking Neural Network Approach to Auditory Source Lateralisation".

 [[paper](https://ieeexplore.ieee.org/document/8683767)]

  * Hui Yan, Xinle Liu, Hong Huo, Tao Fang ***ICONIP 2019***


"Mechanisms of Reward-Modulated STDP and Winner-Take-All in Bayesian Spiking Decision-Making Circuit".

 [[paper](https://link.springer.com/chapter/10.1007%2F978-3-030-36718-3_14)]

  * Yanli Yao, Qiang Yu, Longbiao Wang, Jianwu Dang ***IJCNN 2019***


"A Spiking Neural Network with Distributed Keypoint Encoding for Robust Sound Recognition".

 [[paper](https://ieeexplore.ieee.org/document/8852166)]

  * Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet ***IJCNN 2019*** 


"Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP".

 [[paper](https://ieeexplore.ieee.org/document/8852346)]

 * Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li ***IJCNN 2019***


"Deep Spiking Neural Network with Spike Count based Learning Rule".

 [[paper](https://ieeexplore.ieee.org/document/8852380)]

  * Maximilian P. R. Löhr, Daniel Schmid, Heiko Neumann ***IJCNN 2019***


"Motion Integration and Disambiguation by Spiking V1-MT-MSTl Feedforward-Feedback Interaction".

 [[paper](https://ieeexplore.ieee.org/document/8852029)]

   * Esma Mansouri-Benssassi, Juan Ye ***IJCNN 2019***


"Speech Emotion Recognition With Early Visual Cross-modal Enhancement Using Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8852473)]

 * 	Mikhail Kiselev, Andrey Lavrentyev ***IJCNN 2019***


"A Preprocessing Layer in Spiking Neural Networks - Structure, Parameters, Performance Criteria".

 [[paper](https://ieeexplore.ieee.org/document/8851848)]

 *Won-Mook Kang, Chul-Heung Kim, Soochang Lee, Sung Yun Woo, Jong-Ho Bae, Byung-Gook Park, Jong-Ho Lee ***IJCNN 2019***


"A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices".

 [[paper](https://ieeexplore.ieee.org/document/8851744)]

  * Lyes Khacef, Benoît Miramond, Diego Barrientos, Andres Upegui ***IJCNN 2019***


"Self-organizing neurons: toward brain-inspired unsupervised learning".

 [[paper](https://ieeexplore.ieee.org/document/8852098)]

 * 	Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling ***ICLR 2018***


"Temporally Efficient Deep Learning with Spikes".

 [[paper](https://openreview.net/forum?id=HkZy-bW0-)]

 * 	Aditya Gilra, Wulfram Gerstner ***ICML 2018***


"Non-Linear Motor Control by Local Learning in Spiking Neural Networks".

 [[paper](http://proceedings.mlr.press/v80/gilra18a.html)]

 *Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert A. Legenstein, Wolfgang Maass ***NIPS 2018***


"Long short-term memory and Learning-to-learn in networks of spiking neurons".

 [[paper](http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons)]

 * Sumit Bam Shrestha, Garrick Orchard ***NIPS 2018***


**SLAYER** "Spike Layer Error Reassignment in Time".

 [[paper](http://papers.nips.cc/paper/7415-slayer-spike-layer-error-reassignment-in-time)]

 [[code]( https://github.com/bamsumit/slayerPytorch)]

 * Yu Qi, Jiangrong Shen, Yueming Wang, Huajin Tang, Hang Yu, Zhaohui Wu, Gang Pan ***IJCAI 2018***


Jointly Learning Network Connections and Link Weights in Spiking Neural Networks".

 [[paper](https://www.ijcai.org/Proceedings/2018/221)]

* Qi Xu, Yu Qi, Hang Yu, Jiangrong Shen, Huajin Tang, Gang Pan ***IJCAI 2018***


**CSNN** "An Augmented Spiking based Framework with Perceptron-Inception".

 [[paper](https://www.ijcai.org/Proceedings/2018/228)]

 * Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu ***IJCAI 2018***


**VPSNN** Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu.

 [[paper](https://www.ijcai.org/Proceedings/2018/229)]

  * Alireza Alemi, Christian K. Machens, Sophie Denève, Jean-Jacques E. Slotine ***AAAI 2018***


"Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules".

 [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17438)]

 * Tielin Zhang, Yi Zeng, Dongcheng Zhao, Mengting Shi ***AAAI 2018***


"A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks".

 [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16105)]

  * Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran ***ICASSP 2018***


"Training Probabilistic Spiking Neural Networks with First- To-Spike Decoding".

 [[paper](https://arxiv.org/pdf/1710.10704.pdf)]

   * Qiang Yu, Longbiao Wang, Jianwu Dang ***ICONIP 2018***


"Efficient Multi-spike Learning with Tempotron-Like LTP and PSD-Like LTD".

 [[paper](https://doi.org/10.1007/978-3-030-04167-0_49)]

  * Jiaxing Liu, Guoping Zhao ***IJCNN 2018***


"A bio-inspired SOSNN model for object recognition".

 [[paper](https://ieeexplore.ieee.org/document/8489076/)]

   * Amirhossein Tavanaei, Zachary Kirby, Anthony S. Maida ***IJCNN 2018***


"Training Spiking ConvNets by STDP and Gradient Descent".

 [[paper](https://ieeexplore.ieee.org/document/8489104)]

   * Yu Miao, Huajin Tang, Gang Pan ***IJCNN 2018***


"A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489175)]

 * Timoleon Moraitis, Abu Sebastian, Evangelos Eleftheriou ***IJCNN 2018***


"Spiking Neural Networks Enable Two-Dimensional Neurons and Unsupervised Multi-Timescale Learning".

 [[paper](https://ieeexplore.ieee.org/document/8489218)]

  * Sam Slade, Li Zhang ***IJCNN 2018***


"Topological Evolution of Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489375)]

  *	Ruizhi Chen, Hong Ma, Peng Guo, Shaolin Xie, Pin Li, Donglin Wang ***IJCNN 2018***


"Low Latency Spiking ConvNets with Restricted Output Training and False Spike Inhibition".

 [[paper](https://ieeexplore.ieee.org/document/8489400)]

 * Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet ***IJCNN 2018***


"Mastering the Output Frequency in Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489410)]

  *	Daqi Liu, Shigang Yue ***IJCNN 2018***


"Video-Based Disguise Face Recognition Based on Deep Spiking Neural Network".

 [[paper](https://ieeexplore.ieee.org/document/8489476)]

  * Johannes C. Thiele, Olivier Bichler, Antoine Dupret ***IJCNN 2018***


"A Timescale Invariant STDP-Based Spiking Deep Network for Unsupervised Online Feature Extraction from Event-Based Sensor Data".

 [[paper](https://ieeexplore.ieee.org/document/8489666)]

  * Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma ***IJCNN 2018***


"Unsupervised Learning with Self-Organizing Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489673)]

  * Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó ***IJCNN 2018***


"STDP Learning of Image Patches with Convolutional Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489684)]

   *	Jibin Wu, Yansong Chua, Haizhou Li ***IJCNN 2018***


"A Biologically Plausible Speech Recognition Framework Based on Spiking Neural Networks".

 [[paper](https://ieeexplore.ieee.org/document/8489535)]

 * Antonio Jimeno-Yepes, Jianbin Tang, Benjamin Scott Mashford ***IJCAI 2017***


"Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips".

 [[paper](https://www.ijcai.org/Proceedings/2017/274)]

  * Zhanhao Hu, Tao Wang, Xiaolin Hu ***ICONIP 2017***


"An STDP-Based Supervised Learning Algorithm for Spiking Neural Network".

 [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70096-0_10)]

   * Lin Zuo, Shan Chen, Hong Qu, Malu Zhang ***ICONIP 2017***


"A Fast Precise-Spike and Weight-Comparison Based Learning Approach for Evolving Spiking Neural Networks".

 [[paper](https://link.springer.com/chapter/10.1007%2F978-3-319-70090-8_81)]

   * Amirhossein Tavanaei, Anthony S. Maida ***IJCNN 2017***


"Multi-layer unsupervised learning in a spiking convolutional neural network".

 [[paper](https://ieeexplore.ieee.org/document/7966099)]

   * Takashi Matsubara ***IJCNN 2017***


"Spike timing-dependent conduction delay learning model classifying spatio-temporal spike patterns".

 [[paper](https://ieeexplore.ieee.org/document/7966073)]

   * Laxmi R. Iyer, Arindam Basu ***IJCNN 2017***


"Unsupervised learning of event-based image recordings using spike-timing-dependent plasticity".

 [[paper](https://ieeexplore.ieee.org/document/7966074)]

 * 	Gopalakrishnan Srinivasan, Sourjya Roy, Vijay Raghunathan, Kaushik Roy ***IJCNN 2017***


"Spike timing dependent plasticity based enhanced self-learning for efficient pattern recognition in spiking neural networks".

 [[paper](https://ieeexplore.ieee.org/document/7966075)]

 * Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, Qinru Qiu ***IJCNN 2017***


"Stable spike-timing dependent plasticity rule for multilayer unsupervised and supervised learning".

 [[paper](https://ieeexplore.ieee.org/document/7966096)]

  * Timoleon Moraitis, Abu Sebastian, Irem Boybat, Manuel Le Gallo, Tomas Tuma, Evangelos Eleftheriou ***IJCNN 2017***


"Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes".

 [[paper](https://ieeexplore.ieee.org/document/7966072)]

   * Yingyezhe Jin, Peng Li ***IJCNN 2017***


"Calcium-modulated supervised spike-timing-dependent plasticity for readout training and sparsification of the liquid state machine".

 [[paper](https://ieeexplore.ieee.org/document/7966097)]

 * 	Amirali Amirsoleimani, Majid Ahmadi, Arash Ahmadi ***IJCNN 2017***


"STDP-based unsupervised learning of memristive spiking neural network by Morris-Lecar model".

 [[paper](https://ieeexplore.ieee.org/document/7966284)]
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