Enhancing adaptive history reserving by spiking convolutional block attention module in recurrent neural networks
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-
temporal patterns in time series, such as the Address-Event Representation data collected …
temporal patterns in time series, such as the Address-Event Representation data collected …
Enhancing the robustness of spiking neural networks with stochastic gating mechanisms
Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
Efficient spiking neural networks with sparse selective activation for continual learning
The next generation of machine intelligence requires the capability of continual learning to
acquire new knowledge without forgetting the old one while conserving limited computing …
acquire new knowledge without forgetting the old one while conserving limited computing …
A progressive training framework for spiking neural networks with learnable multi-hierarchical model
Spiking Neural Networks (SNNs) have garnered considerable attention due to their energy
efficiency and unique biological characteristics. However, the widely adopted Leaky …
efficiency and unique biological characteristics. However, the widely adopted Leaky …
Threaten spiking neural networks through combining rate and temporal information
Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …
communities due to their superior spatio-temporal processing capabilities and energy …
Robust decoding of rich dynamical visual scenes with retinal spikes
Sensory information transmitted to the brain activates neurons to create a series of coping
behaviors. Understanding the mechanisms of neural computation and reverse engineering …
behaviors. Understanding the mechanisms of neural computation and reverse engineering …
Robust Stable Spiking Neural Networks
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …
energy budget on neuromorphic hardware. However, they still face challenges in lacking …
Neurosec: Fpga-based neuromorphic audio security
Neuromorphic systems, inspired by the complexity and functionality of the human brain,
have gained interest in academic and industrial attention due to their unparalleled potential …
have gained interest in academic and industrial attention due to their unparalleled potential …
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient
operations and biologically inspired structures, offering potential advantages over Artificial …
operations and biologically inspired structures, offering potential advantages over Artificial …
Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks
The spiking neural networks are inspired by the biological neurons that employ binary
spikes to propagate information in the neural network. It has garnered considerable attention …
spikes to propagate information in the neural network. It has garnered considerable attention …