Training Spiking Neural Networks with Event-driven Backpropagation
Authors: Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, Timothée Masquelier
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that the proposed approach achieves state-of-the-art performance on CIFAR10 among time-based training methods. [...] In this section, we validate the effectiveness of our method on MNIST [48], Fashion-MNIST [49], N-MNIST [50], CIFAR10 [51], and CIFAR100 [51] datasets. This section is organized as follows: We first introduce the training details, then evaluate the performance of our algorithm and compare it with the state-of-the-art event-driven learning approaches. At last, we conduct ablation studies to illustrate the effectiveness of our proposed modules. |
| Researcher Affiliation | Academia | 1School of Computer Science, Peking University 2Institute for Artificial Intelligence, Peking University 3Centre de Recherche Cerveau et Cognition (CERCO), UMR5549 CNRS Univ. Toulouse 3, Toulouse, France |
| Pseudocode | No | The paper describes its learning rules and formulas in text and mathematical notation, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/zhuyaoyu/SNN-event-driven-learning. |
| Open Datasets | Yes | In this section, we validate the effectiveness of our method on MNIST [48], Fashion-MNIST [49], N-MNIST [50], CIFAR10 [51], and CIFAR100 [51] datasets. [...] The datasets we used in this paper are public. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] [...] The test accuracy of these different settings is shown in Tab. 3. |
| Hardware Specification | Yes | We run all experiments on a single Nvidia A100 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | 4.1 Training Details. Initialization: When training in an event-driven fashion... Supervisory signals: Another problem we face is that output neurons corresponding to certain classes do not fire anymore... Experiment settings: In our experiments, we use the real-valued spike current representing the pixel intensities of the image as inputs. [...] Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |