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20202023
most citedFinite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning

10 citations · 30 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.NE2023★ 1 cited

Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network

Duzhen Zhang, Tielin Zhang, Shuncheng Jia +2

Learning from interaction is the primary way that biological agents acquire knowledge about their environment and themselves. Modern deep reinforcement learning (DRL) explores a co…

cs.NE2022

Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect

Shuncheng Jia, Tielin Zhang, Ruichen Zuo +1

Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are conside…

cs.NE2022★ 4 cited

Recent Advances and New Frontiers in Spiking Neural Networks

Duzhen Zhang, Shuncheng Jia, Qingyu Wang

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding met…

cs.NE2022

Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration

Shuncheng Jia, Ruichen Zuo, Tielin Zhang +2

Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the…

cs.NE2020★ 10 cited

Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning

Xiang Cheng, Tielin Zhang, Shuncheng Jia +1

Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artifi…

cs.NE2020★ 3 cited

Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation

Tielin Zhang, Shuncheng Jia, Xiang Cheng +1

Spiking Neural Networks (SNNs) contain more biologically realistic structures and biologically-inspired learning principles than those in standard Artificial Neural Networks (ANNs)…