Classification on SHD

Metric: Percentage correct (higher is better)

LeaderboardDataset

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#	Model↕	Percentage correct▼	Augmentations	Paper	Date↕	Code
1	Event-SSM	95.9	No	Scalable Event-by-event Processing of Neuromorph...	2024-04-29	Code
2	SNN with Dilated Convolution with Learnable Spacings	95.1	No	Learning Delays in Spiking Neural Networks using...	2023-06-30	Code
3	SNN featuring learnable axonal delays with adaptively delay caps	92.45	No	Adaptive Axonal Delays in feedforward spiking ne...	2023-02-16	-
4	CNN	92.4	No	The Heidelberg spiking datasets for the systemat...	2019-10-16	-
5	SNN with spatio-temporal filters and attention	92.4	No	STSC-SNN: Spatio-Temporal Synaptic Connection wi...	2022-10-11	Code
6	SNN with temporal-wise attention	91.1	No	Temporal-wise Attention Spiking Neural Networks ...	2021-07-25	-
7	SNN	87	No	MAP-SNN: Mapping Spike Activities with Multiplic...	2022-04-21	Code
8	Recurrent convolutional SNN	83.5	No	Fluctuation-driven initialization for spiking ne...	2022-06-21	Code
9	Recurrent SNN	83.2	No	The Heidelberg spiking datasets for the systemat...	2019-10-16	-
10	Sparse Spiking Gradient Descent	77.5	No	Sparse Spiking Gradient Descent	2021-05-18	Code
11	FastSNN	70.32	No	Robust and accelerated single-spike spiking neur...	2022-05-30	Code

#1Event-SSMSOTA
95.9
Percentage correct· 2024-04-29
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models Code
#2SNN with Dilated Convolution with Learnable SpacingsSOTA
95.1
Percentage correct· 2023-06-30
Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings Code
#3SNN featuring learnable axonal delays with adaptively delay capsSOTA
92.45
Percentage correct· 2023-02-16
Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition
#4CNNSOTA
92.4
Percentage correct· 2019-10-16
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
#5SNN with spatio-temporal filters and attention
92.4
Percentage correct· 2022-10-11
STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks Code
#6SNN with temporal-wise attention
91.1
Percentage correct· 2021-07-25
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification
#7SNN
87
Percentage correct· 2022-04-21
MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks Code
#8Recurrent convolutional SNN
83.5
Percentage correct· 2022-06-21
Fluctuation-driven initialization for spiking neural network training Code
#9Recurrent SNN
83.2
Percentage correct· 2019-10-16
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
#10Sparse Spiking Gradient Descent
77.5
Percentage correct· 2021-05-18
Sparse Spiking Gradient Descent Code
#11FastSNN
70.32
Percentage correct· 2022-05-30
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks Code