Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …
attention lately due to its promise of reducing the computational energy, latency, as well as …
Gas recognition in E-nose system: A review
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for
recognizing multivariate responses obtained by gas sensors in various applications. Over …
recognizing multivariate responses obtained by gas sensors in various applications. Over …
Brain-inspired neural circuit evolution for spiking neural networks
In biological neural systems, different neurons are capable of self-organizing to form
different neural circuits for achieving a variety of cognitive functions. However, the current …
different neural circuits for achieving a variety of cognitive functions. However, the current …
Brain-inspired computing: A systematic survey and future trends
Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …
theories, models, hardware architectures, and application systems toward more general …
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 …
Exploring temporal information dynamics in spiking neural networks
Abstract Most existing Spiking Neural Network (SNN) works state that SNNs may utilize
temporal information dynamics of spikes. However, an explicit analysis of temporal …
temporal information dynamics of spikes. However, an explicit analysis of temporal …
Research progress of spiking neural network in image classification: a review
Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …
which is more analogous with the brain. It has been widely considered with neural …
Securing deep spiking neural networks against adversarial attacks through inherent structural parameters
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
Adaptive spatiotemporal neural networks through complementary hybridization
Processing spatiotemporal data sources with both high spatial dimension and rich temporal
information is a ubiquitous need in machine intelligence. Recurrent neural networks in the …
information is a ubiquitous need in machine intelligence. Recurrent neural networks in the …