Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks
Authors: Yuchen Tian, Samuel Tensingh, Jason Eshraghian, Nhan Duy Truong, Omid Kavehei
TMLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 24,400 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,767 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | evaluations on benchmarks like CIFAR-10 (+0.42%), CIFAR-100 (+0.99%), CIFAR10-DVS (+0.1%), and Image Net-1K (+0.73%) demonstrated consistent accuracy gains |
| Researcher Affiliation | Collaboration | Yuchen Tian EMAIL School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia; Nhan Duy Truong EMAIL School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia |
| Pseudocode | Yes | Algorithm 1 DA-SSDP (post-step correction; used in our experiments) |
| Open Source Code | Yes | Our code is available at https://github.com/NeuroSyd/DA-SSDP. |
| Open Datasets | Yes | evaluations on benchmarks like CIFAR-10 (+0.42%), CIFAR-100 (+0.99%), CIFAR10-DVS (+0.1%), and Image Net-1K (+0.73%) demonstrated consistent accuracy gains |
| Dataset Splits | Yes | Event-based CIFAR10-DVS is loaded from Spiking Jelly in frame representation with T frames per sample and split 90%/10% for train/test. Backbones follow Spikingresformer with T=4 steps on CIFAR-10/100 (Krizhevsky et al., 2009) and Image Net-1k (Deng et al., 2009) |
| Hardware Specification | Yes | The setup involved a workstation with dual NVIDIA RTX 3090 GPUs, employing PyTorch 1.12.1 with CUDA 11.3 and NumPy 1.24.4. |
| Software Dependencies | Yes | The setup involved a workstation with dual NVIDIA RTX 3090 GPUs, employing PyTorch 1.12.1 with CUDA 11.3 and NumPy 1.24.4. |
| Experiment Setup | Yes | DA-SSDP uses a warm-up of Ewarm=100 (for CIFAR10-DVS, Ewarm=80) epochs to fit the gate, and the fitted gate is then kept fixed for the remaining epochs. Kernel parameters are A+=1.5 10 3, A =1.0 10 4, and a learnable σ. CIFAR-100: Model: spikingresformer_cifar; input: 3 32 32; epochs: 600; batch size: 200; T: 4; optimizer: Adam W with lr = 5e 4 and weight decay 0.01; augmentation: Rand Augment rand-m7-n1-mstd0.5-inc1; mixup: on; cutout: off; label smoothing: 0.1; AMP: on; Sync BN: off. |