103 citations · 253 across the 7 of their papers we have counts for
8 papers
Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learning
Shikuang Deng, Jiayuan Zhang, Yuhang Wu +2
Hebbian learning is a biological principle that intuitively describes how neurons adapt their connections through repeated stimuli. However, when applied to machine learning, it su…
Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness
Kangrui Du, Yuhang Wu, Shikuang Deng +1
Spiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with cu…
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…
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…
Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity
Shikuang Deng, Jingwei Li, B. T. Thomas Yeo +1
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical…
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…
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…
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,…