1 code implementation • 29 Dec 2022 • Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Qingyu Wang, Bo Xu
Learning from the interaction is the primary way biological agents know about the environment and themselves.
1 code implementation • 12 Nov 2022 • Shuncheng Jia, Tielin Zhang, Ruichen Zuo, Bo Xu
Here, we propose a Motif-topology improved SNN (M-SNN) for the efficient multi-sensory integration and cognitive phenomenon simulations.
1 code implementation • 12 Mar 2022 • 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 methods, and event-driven characteristics that naturally fit the neuromorphic hardware.
1 code implementation • 11 Feb 2022 • Shuncheng Jia, Ruichen Zuo, Tielin Zhang, Hongxing Liu, Bo Xu
Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs).
no code implementations • 15 Jun 2021 • Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Xiang Cheng, Bo Xu
Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding.
1 code implementation • 9 Oct 2020 • Tielin Zhang, Shuncheng Jia, Xiang Cheng, Bo Xu
The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art BP-based SNNs and saved 50% more computational cost than ANNs.
no code implementations • 7 Oct 2020 • Xiang Cheng, Tielin Zhang, Shuncheng Jia, Bo Xu
Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artificial and natural neural networks.