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 [1].
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. |