spiking neural networks
A class of neural networks that mimic the behavior of biological neurons by transmitting information as discrete spikes over time. They are particularly suited for applications requiring temporal processing and energy efficiency.
- A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
- Activity Pruning for Efficient Spiking Neural Networks
- Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks
- Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
- Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
- Bipolar Self-attention for Spiking Transformers
- Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training
- HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses
- Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
- MI-TRQR: Mutual Information-Based Temporal Redundancy Quantification and Reduction for Energy-Efficient Spiking Neural Networks
- Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
- Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control
- S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
- S$^2$NN: Sub-bit Spiking Neural Networks
- SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
- STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
- Spik-NeRF: Spiking Neural Networks for Neural Radiance Fields
- Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
- Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks
- Spiking Neural Networks Need High-Frequency Information
- SpikingVTG: A Spiking Detection Transformer for Video Temporal Grounding
- Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
- Toward Relative Positional Encoding in Spiking Transformers
- Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks