Neuronal Competition Groups with Supervised STDP for Spike-Based Classification
Authors: Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On top of two different unsupervised feature extractors, we obtain significant accuracy improvements on image recognition datasets such as CIFAR-10 and CIFAR-100. We show that our competition regulation mechanism is crucial for ensuring balanced competition and improved class separation. |
| Researcher Affiliation | Academia | Gaspard Goupy1, Pierre Tirilly1, and Ioan Marius Bilasco1,* 1Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille, France |
| Pseudocode | Yes | In Supplementary Material (Section 1), we provide the overall algorithm for training a spiking classification layer with our proposed methods. |
| Open Source Code | Yes | The source code is publicly available at: https://gitlab.univ-lille.fr/fox/snn-ncg. |
| Open Datasets | Yes | We select four image recognition datasets of growing complexity: MNIST [49], Fashion-MNIST [50], CIFAR-10 [51], and CIFAR-100 [51]. MNIST and Fashion-MNIST comprise 28 28 grayscale images, 60, 000 samples for training and 10, 000 for testing, categorized into 10 classes. CIFAR-10 and CIFAR-100 comprise 32 32 RGB images, 50, 000 for training and 10, 000 for testing. They consist of, respectively, 10 and 100 classes. |
| Dataset Splits | Yes | For hyperparameter optimization, we construct a validation set from the training set by randomly selecting, for each class, a percentage ν of its samples. Then, we use the gridsearch algorithm to optimize the hyperparameters of the spiking classification layer (for each rule, dataset, and feature extractor). For evaluation, we employ the K-fold cross-validation strategy. We divide the training set into K subsets and train K models, each using a different subset for validation while the remaining K 1 subsets are used for training. Each model is trained with a different seed. Then, we evaluate the trained models on the test set and we compute the mean test accuracy and standard deviation (1-sigma). We use ρ = 10, K = 10 and ν = 1 K (i.e. we allocate 10% of the training sets for validation). |
| Hardware Specification | Yes | Experiments presented in this paper were carried out using the Grid 5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). (Supplementary Material Section 2.4 further states: 'All experiments presented in this paper were carried out using the Grid 5000 testbed [58]. They were mainly run on CPU (Intel Xeon E5-2630 v3 @ 2.40GHz) but some of the longer experiments (i.e. CIFAR-100, ablation studies, hyperparameter optimization) were run on GPU (NVIDIA A100).') |
| Software Dependencies | No | The paper discusses various software components and libraries, but it does not provide specific version numbers for these software dependencies, which are necessary for reproducible descriptions within the provided text. |
| Experiment Setup | Yes | 5.1 Experimental Setup: Our classification system consist of a feature extractor trained with unsupervised Hebbian-based learning, followed by a spiking classification layer trained with supervised STDP. Unless otherwise specified, we set M = 5 neurons per class for NCG-based methods... For hyperparameter optimization, we construct a validation set from the training set by randomly selecting, for each class, a percentage ν of its samples. Then, we use the gridsearch algorithm to optimize the hyperparameters of the spiking classification layer... We use ρ = 10, K = 10 and ν = 1 K (i.e. we allocate 10% of the training sets for validation). |