Priority coding in the visual system
Although we are continuously bombarded with visual input, only a fraction of incoming visual
events is perceived, remembered or acted on. The neural underpinnings of various forms of …
events is perceived, remembered or acted on. The neural underpinnings of various forms of …
Temporal effective batch normalization in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are
investigated as biologically plausible and high-performance models of neural computation …
investigated as biologically plausible and high-performance models of neural computation …
Towards a new paradigm for brain-inspired computer vision
Brain-inspired computer vision aims to learn from biological systems to develop advanced
image processing techniques. However, its progress so far is not impressing. We recognize …
image processing techniques. However, its progress so far is not impressing. We recognize …
Optimized potential initialization for low-latency spiking neural networks
Abstract Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and adversarial …
distinctive properties of low power consumption, biological plausibility, and adversarial …
Self-supervised learning of event-based optical flow with spiking neural networks
J Hagenaars, F Paredes-Vallés… - Advances in Neural …, 2021 - proceedings.neurips.cc
The field of neuromorphic computing promises extremely low-power and low-latency
sensing and processing. Challenges in transferring learning algorithms from traditional …
sensing and processing. Challenges in transferring learning algorithms from traditional …
Training spiking neural networks with event-driven backpropagation
Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …
Exploring loss functions for time-based training strategy in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training
Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …
critical applications with the advance of neuromorphic computing. Recent work has …
Rate gradient approximation attack threats deep spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …