Brain-inspired Balanced Tuning for Spiking Neural Networks
Authors: Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is verified on the MNIST hand-written digit recognition dataset and the performance (the accuracy of 98.64%) indicates that the ideas of balancing state could indeed improve the learning ability of SNNs... |
| Researcher Affiliation | Academia | 1 Institute of Automation, Chinese Academy of Sciences (CAS), China 2 University of Chinese Academy of Sciences, China 3 Research Center for Brain-inspired Intelligence, Institute of Automation, CAS, China 4 National Laboratory of Pattern Recognition, Institute of Automation, CAS, China 5 Center for Excellence in Brain Science and Intelligence Technology, CAS, China {tielin.zhang, yi.zeng}@ia.ac.cn |
| Pseudocode | Yes | Algorithm 1 The Balanced SNN Learning Algorithm. |
| Open Source Code | No | The paper does not provide any statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | Yes | We use the standard MNIST [Le Cun, 1998] to test the proposed brain-inspired Balanced SNN model. MNIST contains 10 classes of handwritten digits with 60, 000 training samples and 10, 000 test samples. |
| Dataset Splits | No | MNIST contains 10 classes of handwritten digits with 60, 000 training samples and 10, 000 test samples. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | No | Algorithm 1 mentions the initialization of learning rates ηMP, η0, η1, ηST DP, and ηc, but their specific values are not provided in the paper. Other experimental setup details such as batch size or number of epochs are also not specified. |