Huajin Tang
教授
Develops spiking neural networks, neuromorphic computing and brain-inspired cognitive models, including neuromorphic hardware and robotics applications.
Huajin Tang's lab at Zhejiang works on computational neuroscience & cognition and accelerators, chips & architecture. 92 papers since 2023, 38 at top venues, mostly AAAI and CVPR. Applications close .
At a glance
- papers, 2023–2026
- 24/ year
- at T1 venues
- 41%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 17 papers2026 · T1 10 · collecting
- 26 papers2025 · T1 14
- 29 papers2024 · T1 6
- 20 papers2023 · T1 8
T1 venuesOtherVenues: AAAI, CVPR, IJCNN, Neural Networks, Neurocomputing
Research focuspaper titles that use each topic's words
- neuromorphic4 papers
- Accelerators, chips & architecture2 papers
- neuromorphic computing1 paper
- Computational neuroscience & cognition0 papers
- cognitive models0 papers
Paper list
From DBLP.
202617 papers
- IEEE Comput. Intell. Mag.CIS Publication Spotlight [Publication Spotlight] (opens in a new tab)
- PatternsDendritic nonlinearities mitigate communication costs (opens in a new tab)
- CVPR (T1)Dynamic-Static Decomposition for Novel View Synthesis of Dynamic Scenes with Spiking Neurons (opens in a new tab)
- IEEE Trans. Cogn. Dev. Syst.Editorial: 2026 New Year Message From the Editor-in-Chief (opens in a new tab)
- CVPR (T1)eRetinexGS: Retinex Modeling for Low-Light Scene Enhancement via Event Streams and 3D Gaussian Splatting (opens in a new tab)
- ICICMAS-SNN: Adaptive Sampling and Representation via Memory-Augmented Recurrent Spiking Neural Networks for Event-Based Object Detection (opens in a new tab)
- AAAI (T1)MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization (opens in a new tab)
- NeurocomputingMSER: Multi-scale event representation model for enhanced spatio-temporal feature extraction (opens in a new tab)
- CVPR (T1)On the Role of Temporal Granularity in the Robustness of Spiking Neural Networks (opens in a new tab)
- TIP (T1)OT-SAN: One Timestep Spiking Actor Network With Mask-Adaptive Global Connection and Dynamic Thresholds (opens in a new tab)
- CVPR (T1)Robust Spiking Neural Networks by Temporal Mutual Information (opens in a new tab)
- AAAI (T1)S³: Spiking Neurons as an Isolating Segmenter for Brain Signal Decoding (opens in a new tab)
202526 papers
- ICASSP (T1)Adaptive Gradient-Based Timesurface for Event-based Detection (opens in a new tab)
- AAAI (T1)ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning (opens in a new tab)
- ICRA (T1)Brain-Inspired Spatial Continuous State Encoding for Efficient Spiking-Based Navigation (opens in a new tab)
- ICDE (T1)CAM: Asynchronous GPU-Initiated, CPU-Managed SSD Management for Batching Storage Access (opens in a new tab)
- Knowl. Based Syst.Context gating in spiking neural networks: Achieving lifelong learning through integration of local and global plasticity (opens in a new tab)
- IJCNNDeep Spiking Neural Network with Adaptive Temporal Feature Fusion for Energy-efficient Event-based Person Re-Identification (opens in a new tab)
- ICCV (T1)E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes (opens in a new tab)
- IEEE Trans. Cogn. Dev. Syst.Editorial: 2025 New Year Message From the Editor-in-Chief (opens in a new tab)
- IJCAI (T1)EDyGS: Event Enhanced Dynamic 3D Radiance Fields from Blurry Monocular Video (opens in a new tab)
- AAAI (T1)EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting (opens in a new tab)
- AAAI (T1)EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events (opens in a new tab)
- AAAI (T1)EvSTVSR: Event Guided Space-Time Video Super-Resolution (opens in a new tab)
202429 papers
- IEEE Trans. Instrum. Meas.A High-Precision LiDAR-Inertial Odometry via Invariant Extended Kalman Filtering and Efficient Surfel Mapping (opens in a new tab)
- IJCNNAdaptive Multi-Level Firing for Direct Training Deep Spiking Neural Networks (opens in a new tab)
- IJCNNAn Event-based Feature Representation Method for Event Stream Classification using Deep Spiking Neural Networks (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Attention-Based Deep Spiking Neural Networks for Temporal Credit Assignment Problems (opens in a new tab)
- Proc. IEEEBrain-Inspired Computing: A Systematic Survey and Future Trends (opens in a new tab)
- IROS (T1)CASRL: Collision Avoidance with Spiking Reinforcement Learning Among Dynamic, Decision-Making Agents (opens in a new tab)
- CIS-RAMCLFR-M: Continual Learning Framework for Robots via Human Feedback and Dynamic Memory (opens in a new tab)
- Proc. IEEECorrections to "Brain-Inspired Computing: A Systematic Survey and Future Trends" (opens in a new tab)
- ECCV (T1)EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-Based Detection with Recurrent Spiking Neural Networks (opens in a new tab)
- IEEE Trans. Cogn. Dev. Syst.Editorial IEEE Transactions on Cognitive and Developmental Systems (opens in a new tab)
- Frontiers Comput. Neurosci.Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Effective Active Learning Method for Spiking Neural Networks (opens in a new tab)
202320 papers
- IEEE Trans. Neural Networks Learn. Syst.A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design (opens in a new tab)
- IJCAI (T1)A Low Latency Adaptive Coding Spike Framework for Deep Reinforcement Learning (opens in a new tab)
- ICML (T1)Adaptive Smoothing Gradient Learning for Spiking Neural Networks (opens in a new tab)
- IJCNNBipolar Population Threshold Encoding for Audio Recognition with Deep Spiking Neural Networks (opens in a new tab)
- ICONIPCMCI: A Robust Multimodal Fusion Method for Spiking Neural Networks (opens in a new tab)
- CVPR (T1)Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation (opens in a new tab)
- Neural NetworksDual memory model for experience-once task-incremental lifelong learning (opens in a new tab)
- NeurIPS (T1)Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks (opens in a new tab)
- AAAI (T1)ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks (opens in a new tab)
- Memetic Comput.Event-driven spiking neural networks with spike-based learning (opens in a new tab)
- PatternsExploiting noise as a resource for computation and learning in spiking neural networks (opens in a new tab)
- Neural Comput. Appl.Grid cell modeling with mapping representation of self-motion for path integration (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
Similar labsnearest by research summary
- CHEPFL
Martin Schrimpf
- Neuroscience & brain-computer interfaces
- DETUM
Luise Pufahl
- Physics-based simulation & animation
- USUIUC
Charles L. Isbell, Jr.
- Reinforcement learning
- CHEPFL
Olga Fink
- Accelerators, chips & architecture
- CHEPFL
Devis Tuia
- Physics, climate & scientific computing
- USUT Austin
Chenfeng Xu
- Efficient training & inference
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- The lab site and the paper record do not agree.
- Collected
- Verified
- Papers
- DBLP
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Zhejiangcloses