Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns
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[1] Lei Deng,et al. Attention Spiking Neural Networks , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Wei Fang,et al. Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks , 2023, ICLR.
[3] Yiting Dong,et al. BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation , 2022, SSRN Electronic Journal.
[4] Yule Duan,et al. TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks , 2022, IEEE transactions on neural networks and learning systems.
[5] Liwen Zhang,et al. RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Shenmin Zhang,et al. Gradual Surrogate Gradient Learning in Deep Spiking Neural Networks , 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[7] Yi Zeng,et al. Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes , 2022, IJCAI.
[8] Shanghang Zhang,et al. Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting , 2022, ICLR.
[9] Sungroh Yoon,et al. AutoSNN: Towards Energy-Efficient Spiking Neural Networks , 2022, ICML.
[10] P. Panda,et al. Neural Architecture Search for Spiking Neural Networks , 2022, ECCV.
[11] Jihang Wang,et al. Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules , 2021, Inf. Sci..
[12] Dongcheng Zhao,et al. Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks , 2021, Patterns.
[13] Yi Zeng,et al. BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons , 2021, Neural Networks.
[14] Yi Zeng,et al. BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons , 2021, Frontiers in Neuroscience.
[15] Guoqi Li,et al. LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[16] Kay Chen Tan,et al. Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[17] K. Roy,et al. DIET-SNN: A Low-Latency Spiking Neural Network With Direct Input Encoding and Leakage and Threshold Optimization , 2021, IEEE Transactions on Neural Networks and Learning Systems.
[18] Guoqi Li,et al. Temporal-wise Attention Spiking Neural Networks for Event Streams Classification , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Lei Deng,et al. Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning , 2021, IJCAI.
[20] Malu Zhang,et al. Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information , 2021, AAAI.
[21] Sander M. Bohte,et al. Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks , 2021, Nature Machine Intelligence.
[22] Yonghong Tian,et al. Deep Residual Learning in Spiking Neural Networks , 2021, NeurIPS.
[23] Yujie Wu,et al. Going Deeper With Directly-Trained Larger Spiking Neural Networks , 2020, AAAI.
[24] Peng Li,et al. Skip-Connected Self-Recurrent Spiking Neural Networks With Joint Intrinsic Parameter and Synaptic Weight Training , 2020, Neural Computation.
[25] P. Panda,et al. Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch , 2020, Frontiers in Neuroscience.
[26] Yonghong Tian,et al. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Idan Segev,et al. Single cortical neurons as deep artificial neural networks , 2019, Neuron.
[28] Shanghang Zhang,et al. Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks , 2021, NeurIPS.
[29] O. Papaspiliopoulos. High-Dimensional Probability: An Introduction with Applications in Data Science , 2020 .
[30] Kaushik Roy,et al. DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks , 2020, ArXiv.
[31] Bo Xu,et al. LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition , 2020, IJCAI.
[32] Elisabetta Chicca,et al. Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks , 2020, Frontiers in Neuroscience.
[33] Kaushik Roy,et al. Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation , 2020, ICLR.
[34] Wenrui Zhang,et al. Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks , 2020, NeurIPS.
[35] Antoine Dupret,et al. SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes , 2019, ICLR.
[36] Hong Yang,et al. DART: Distribution Aware Retinal Transform for Event-Based Cameras , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Lei Deng,et al. Direct Training for Spiking Neural Networks: Faster, Larger, Better , 2018, AAAI.
[38] Sungroh Yoon,et al. Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks , 2018, 2019 56th ACM/IEEE Design Automation Conference (DAC).
[39] Hao Li,et al. Visualizing the Loss Landscape of Neural Nets , 2017, NeurIPS.
[40] Lei Deng,et al. Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks , 2017, Front. Neurosci..
[41] Luping Shi,et al. CIFAR10-DVS: An Event-Stream Dataset for Object Classification , 2017, Front. Neurosci..
[42] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Gregory Cohen,et al. Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades , 2015, Front. Neurosci..
[44] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[45] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[46] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[47] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[48] Eugene M. Izhikevich,et al. Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.
[49] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[50] Frank C. Hoppensteadt,et al. Bursts as a unit of neural information: selective communication via resonance , 2003, Trends in Neurosciences.
[51] Adam Kepecs,et al. Information encoding and computation with spikes and bursts , 2003, Network.
[52] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[53] S. Sherman,et al. Fourier analysis of sinusoidally driven thalamocortical relay neurons and a minimal integrate-and-fire-or-burst model. , 2000, Journal of neurophysiology.
[54] J. Lisman. Bursts as a unit of neural information: making unreliable synapses reliable , 1997, Trends in Neurosciences.
[55] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.
[56] R. Winder. Partitions of N-Space by Hyperplanes , 1966 .