8 accepted papers
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 adversarial attacks. Although spike coding strategies and neural dynamics parameters have been extensively studied for their
With their high dynamic range and temporal resolution, event cameras are well-suited for object detection, especially under motion blur and extreme illumination. Recent state-of-the-art works for event-based object detection primarily focus on the high-level design of backbones. However, developing
Recent advancements have focused on directly training high-performance spiking neural networks (SNNs) by estimating the approximate gradients of spiking activity through a continuous function with constant sharpness, known as surrogate gradient (SG) learning. However, as spikes propagate within neur
Spiking neural networks (SNNs) show great potential in mapless navigation tasks due to their low power consumption, but the continuous representation of spatial information poses a challenge to SNN training. Neuroscience findings reveal that spatial cognition cells encode spatial information through
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 significantly reduce the computational resource requirements for agents and improve the algorithm's performance under re…
"Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection in scenarios with motion blur and challenging lighting conditions. However, while most existing approaches prioritize optimizing spatiotemporal representations with advanced detection backb…
Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics demonstrate superior energy efficiency on neuromorphic architectures. Error backpropagation in SNNs is prohibited by the all-or-none nature of spikes. The existing solution circumvents this problem by a relaxation on…
Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an **unrealistic and inflexible processing paradigm**. Meanwh…