20 citations · 44 across the 6 of their papers we have counts for
10 papers
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…
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…