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].
A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks
Authors: Tielin Zhang, Yi Zeng, Dongcheng Zhao, Mengting Shi
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we get the accuracy of 98.52% on the hand-written digits classification task on MNIST. |
| Researcher Affiliation | Academia | 1Institute of Automation, Chinese Academy of Sciences, Beijing, China 2Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China 3University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1 The Algorithm of SNN Learning. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The Modified National Institute of Standards and Technology (MNIST) dataset with ten classes of hand-written digits (from zero to nine) is used to test the performance of the proposed SNN algorithm. |
| Dataset Splits | No | Here we use standard 60,000 MNIST data to train and another 10,000 to test (no cross validation). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set iteration time as 100, the patch size as 10, and the learning rate as 0.05. |