3 citations · 6 across the 3 of their papers we have counts for
7 papers · 1 filter
Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization
Wenrui Zhang, Hejia Geng, Peng Li
In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed…
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
Yukun Yang, Wenrui Zhang, Peng Li
While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss o…
Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training
Wenrui Zhang, Peng Li
As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequen…
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Wenrui Zhang, Peng Li
Spiking neural networks (SNNs) are well suited for spatio-temporal learning and implementations on energy-efficient event-driven neuromorphic processors. However, existing SNN erro…
Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
Changqing Xu, Wenrui Zhang, Yu Liu +1
Spiking neural networks (SNNs) are the third generation of neural networks and can explore both rate and temporal coding for energy-efficient event-driven computation. However, the…
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
Wenrui Zhang, Peng Li
Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient event-driven hardware neuromorphic processors. As an important class of SNNs, recurrent spi…