70 citations · 168 across the 17 of their papers we have counts for
17 papers
MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
Wei Hua, Chenlin Zhou, Jibin Wu +2
The combination of Spiking Neural Networks (SNNs) with Vision Transformer architectures has garnered significant attention due to their potential for energy-efficient and high-perf…
Emergent Bio-Functional Similarities in a Cortical-Spike-Train-Decoding Spiking Neural Network Facilitate Predictions of Neural Computation
Tengjun Liu, Yansong Chua, Yiwei Zhang +6
Despite its better bio-plausibility, goal-driven spiking neural network (SNN) has not achieved applicable performance for classifying biological spike trains, and showed little bio…
Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition
Pengfei Sun, Ehsan Eqlimi, Yansong Chua +2
Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems. Recent advances in audio-to-spike encoding a…
Training Spiking Neural Networks with Local Tandem Learning
Qu Yang, Jibin Wu, Malu Zhang +3
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized tr…
Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks
Lei Jiang, Yongqing Liu, Shihai Xiao +1
Lateral inhibitory connections have been observed in the cortex of the biological brain, and has been extensively studied in terms of its role in cognitive functions. However, in t…
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks
Timoleon Moraitis, Dmitry Toichkin, Adrien Journé +2
Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for neuromorphic on-chip learning, owing to its efficient, local, unsupervised, and on-line nature. Moreov…
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
Malu Zhang, Jiadong Wang, Burin Amornpaisannon +8
Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-po…
Transfer Learning in General Lensless Imaging through Scattering Media
Yukuan Yang, Lei Deng, Peng Jiao +4
Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational ima…
Neural Population Coding for Effective Temporal Classification
Zihan Pan, Jibin Wu, Yansong Chua +2
Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve…
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
Zihan Pan, Yansong Chua, Jibin Wu +3
Auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audi…
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Jibin Wu, Yansong Chua, Malu Zhang +3
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentia…
Hardware-friendly Neural Network Architecture for Neuromorphic Computing
Roshan Gopalakrishnan, Yansong Chua, Ashish Jith Sreejith Kumar
The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such a…
Deep Spiking Neural Network with Spike Count based Learning Rule
Jibin Wu, Yansong Chua, Malu Zhang +3
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synch…
MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses
Roshan Gopalakrishnan, Ashish Jith Sreejith Kumar, Yansong Chua
Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in…
Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain
Laxmi R. Iyer, Yansong Chua, Haizhou Li
The advantage of spiking neural networks (SNNs) over their predecessors is their ability to spike, enabling them to use spike timing for coding and efficient computing. A neuromorp…
Classifying neuromorphic data using a deep learning framework for image classification
Roshan Gopalakrishnan, Yansong Chua, Laxmi R Iyer
In the field of artificial intelligence, neuromorphic computing has been around for several decades. Deep learning has however made much recent progress such that it consistently o…
Neurogenesis and multiple plasticity mechanisms enhance associative memory retrieval in a spiking network model of the hippocampus
Yansong Chua, Cheston Tan
Hippocampal CA3 is crucial for the formation of long-term associative memory. It has a heavily recurrent connectivity, and memories are thought to be stored as memory engrams in th…