Evolving Connectivity for Recurrent Spiking Neural Networks
Authors: Guan Wang, Yuhao Sun, Sijie Cheng, Sen Song
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate EC on a series of standard robotic locomotion tasks, where it achieves comparable performance with deep neural networks and outperforms gradient-trained RSNNs, even solving the complex 17-Do F humanoid task. |
| Researcher Affiliation | Academia | Guan Wang1, 2 , Yuhao Sun2, 3 , Sijie Cheng1, 4, Sen Song2, 3 1Deptartment of Computer Science and Technology, Tsinghua University 2Laboratory of Brain and Intelligence, Tsinghua University 3Department of Biomedical Engineering, Tsinghua University 4Institute for AI Industry Research (AIR), Tsinghua University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/imoneoi/Evolving Connectivity . |
| Open Datasets | Yes | Tasks. We focus on three robotic locomotion tasks in our experiments, Humanoid, Walker2d, and Hopper, as they are commonly used for sequential decision-making problems in the reinforcement learning domain [Brockman et al., 2016, Freeman et al., 2021]. |
| Dataset Splits | No | The paper focuses on evaluating performance on reinforcement learning tasks rather than using explicit train/validation/test dataset splits. There are no mentions of specific validation sets or how data was partitioned for validation. |
| Hardware Specification | Yes | As a result, the training process achieves over 180, 000 frames per second on a single NVIDIA TITAN RTX GPU. [...] 8x NVIDIA Titan RTX GPU (24GB VRAM) 2x Intel(R) Xeon(R) Silver 4110 CPU 252GB Memory |
| Software Dependencies | No | Our EC framework and all baselines are implemented using the JAX library [Bradbury et al., 2018] and just-in-time compiled with the Brax physics simulator [Freeman et al., 2021] for efficient GPU execution. The paper mentions JAX and Brax but does not provide specific version numbers for these libraries. |
| Experiment Setup | Yes | Each experiment s result is averaged over 3 independent seeds, with the standard deviation displayed as a shaded area. For detailed information on hyperparameters and hardware specifications, please refer to Appendix G. [...] Appendix G.2 Hyperparameters lists specific values in Table 3, 4, 5, and 6. |