activity
20202022
most citedTemporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

103 citations · 237 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NE202213 cited

Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration

Yuhang Li, Shikuang Deng, Xin Dong +1

Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtain…

cs.NE2022103 cited

Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Shikuang Deng, Yuhang Li, Shanghang Zhang +1

Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Still, it is…

cs.LG202122 cited

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

Yuhang Li, Shikuang Deng, Xin Dong +2

Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the…

cs.NE202199 cited

Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Shikuang Deng, Shi Gu

Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process asynchronous discrete signals. While more efficien…

q-bio.QM2020

Controllability Analysis of Functional Brain Networks

Shikuang Deng, Shi Gu

Network control theory has recently emerged as a promising approach for understanding brain function and dynamics. By operationalizing notions of control theory for brain networks,…