70 citations · 222 across the 40 of their papers we have counts for
6 papers · 1 filter
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
Shimin Zhang, Qu Yang, Chenxiang Ma +3
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…
Long Short-term Memory with Two-Compartment Spiking Neuron
Shimin Zhang, Qu Yang, Chenxiang Ma +3
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
Xinyi Chen, Qu Yang, Jibin Wu +2
Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneo…
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…
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…
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…