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].
On the Intrinsic Structures of Spiking Neural Networks
Authors: Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments conducted on various real-world datasets affirm the effectiveness of our proposed methods. |
| Researcher Affiliation | Academia | 1 National Key Laboratory for Novel Software Technology, Nanjing University, China 2 School of Intelligent Science and Technology, Nanjing University, China 3 School of Artificial Intelligence, Nanjing University, China 4 School of Mathematical Sciences, Jiangsu Second Normal University, China 5 Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, UAE |
| Pseudocode | Yes | Algorithm 1 Algorithmic Calculation for Upper Bounds of H(n). |
| Open Source Code | No | The paper does not explicitly state that the authors' implementation code is open-source or provide a link to a repository. Mentions of licenses refer to the paper itself, not associated code. |
| Open Datasets | Yes | The Neuromorphic-MNIST (N-MNIST) data set1 (Orchard et al., 2015) is a spiking version of the original frame-based MNIST data set. ... The CIFAR10-DVS data set (Li et al., 2017) is an event-stream conversion of CIFAR-10 ... The DVS128-Gesture data set (Amir et al., 2017) comprises 1,342 instances ... The MNIST handwritten digit data set2 comprises a training set of 60,000 examples ... The Fashion-MNIST data set3 consists of a training set of 60,000 examples ... The Extended MNIST-Balanced (EMNIST) (Cohen et al., 2017) data set is an extension of MNIST ... The CIFAR-10 data set (Krizhevsky and Hinton, 2009) consists of 60000 32x32 color images ... The CIFAR-100 data set is just like the CIFAR-10, except it has 100 classes ... |
| Dataset Splits | Yes | N-MNIST: It consists of the same 60,000 training and 10,000 testing samples as the original MNIST data set ... MNIST: comprises a training set of 60,000 examples and a testing set of 10,000 examples ... Fashion-MNIST: consists of a training set of 60,000 examples and a testing set of 10,000 examples. ... EMNIST: contains ... 112,800 training and 18,800 testing samples for 47 classes. ... CIFAR-10: consists of 60000 32x32 color images in 10 classes, with 50000 training images and 10000 test images. ... CIFAR-100: each class contains 600 (500 training and 100 testings) images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'efficient software packages for their training and deployment' but does not specify any particular software names with version numbers that would be required to replicate the experiments. |
| Experiment Setup | Yes | Table 3: Hyper-parameter setting of the proposed SNNs on image recognition. (Lists Batch Size, Encoding Length T, Expect Spike Count (True/False), Firing Threshold, Learning Rate η, Excitation Probability Threshold pθ, Maximum Time, Membrane Time τm, Time Constant of Synapse τs, Time Step τs) |