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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Authors: Jinseok Kim, Kyungsu Kim, Jae-Joon Kim
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results showed that the proposed method achieves higher performance in terms of both accuracy and efficiency than the previous approaches. In experiments with random spike-train matching task and widely used benchmarks (MNIST and N-MNIST), our method achieved the higher accuracy than that of existing methods when the networks are forced to use fewer spikes in training. |
| Researcher Affiliation | Academia | Jinseok Kim1 Kyungsu Kim1 Jae-Joon Kim1,2 1Department of Creative IT Engineering, 2Graduate School of Artificial Intelligence Pohang University of Science and Technology (POSTECH), Korea EMAIL |
| Pseudocode | No | The paper contains mathematical formulations and diagrams but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | 1The source code is available at https://github.com/Kyungsu Kim42/ANTLR. |
| Open Datasets | Yes | latency-coded MNIST [26] and spiking version of MNIST, called N-MNIST [27]. The references [26] and [27] provide proper citations: '[26] Y. Le Cun, C. Cortes, and C. J. Burges, The mnist database of handwritten digits, 1998, URL http://yann. lecun. com/exdb/mnist, vol. 10, p. 34, 1998.' and '[27] G. Orchard, A. Jayawant, G. K. Cohen, and N. Thakor, Converting static image datasets to spiking neuromorphic datasets using saccades, Frontiers in neuroscience, vol. 9, p. 437, 2015.' |
| Dataset Splits | Yes | We trained the network with a size of 784-800-10 and 100 time steps using a mini-batch size of 16 and the split of 50000/10000 images for training/validation dataset. |
| Hardware Specification | No | The paper mentions 'CUDA-compatible gradient computation functions' but does not specify any particular hardware details such as GPU models, CPU types, or memory used for experiments. |
| Software Dependencies | No | The paper states 'implemented CUDA-compatible gradient computation functions in PyTorch [23]', but it does not specify explicit version numbers for PyTorch, CUDA, or any other software dependencies. |
| Experiment Setup | Yes | We trained the network with a size of 784-800-10 and 100 time steps using a mini-batch size of 16 and We trained the network with a size of 2x34x34-800-10 using a mini-batch size of 16. |