2 citations · 4 across the 6 of their papers we have counts for
7 papers
MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization
Runhao Jiang, Chengzhi Jiang, Rui Yan +1
The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adve…
Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics
Jiaqiang Jiang, Lei Wang, Runhao Jiang +2
Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Recent advancements have focused on d…
ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network
Shang Xu, Jiayu Zhang, Ziming Wang +3
In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also int…
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
Shibo Zhou, Bo Yang, Mengwen Yuan +4
Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable a…
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL
Lang Qin, Ziming Wang, Runhao Jiang +2
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can…
EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks
Ziming Wang, Ziling Wang, Huaning Li +4
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting…
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
Gehua Ma, Runhao Jiang, Rui Yan +1
Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temp…