References

Notation:    Conversion   Direct Training    Hybrid    Reproduced

Notation	Author(s)	Title	Publisher	Cutoff	Link to Paper
2024
STR	Wu et al.	Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network	arXiv preprint arXiv:2405.00699	✓	Link
2023
RCS	Wu et al.	Optimising event-driven spiking neural network with regularisation and cutoff	arXiv preprint arXiv:2301.09522	✓	Link
SpikeCP	Chen et al.	SpikeCP: Delay-adaptive reliable spiking neural networks via conformal prediction	arXiv preprint arXiv:2305.11322	✓	Link
2022
ECC	Wu et al.	A little energy goes a long way: Build an energy-efficient, accurate spiking neural network from convolutional neural network	Frontiers in neuroscience	✗	Link
QCFS	Bu et al.	Optimal {ANN}-{SNN} Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks	ICLR	✗	Link
TET	Deng et al.	Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting	ICLR	✗	Link
TEBN	Duan et al.	Temporal effective batch normalization in spiking neural networks	Advances in Neural Information Processing Systems	✗	Link
2019
ThresholdNorm	Sengupta et al.	Going deeper in spiking neural networks: VGG and residual architectures	Frontiers in neuroscience	✗	Link
2017
WeightNorm	Rueckauer et al.	Conversion of continuous-valued deep networks to efficient event-driven networks for image classification	Frontiers in neuroscience	✗	Link