Exploring Temporal Information Dynamics in Spiking Neural Networks
Authors: Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer, Priyadarshini Panda
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We observe that the temporal information concentration phenomenon is a common learning feature of SNNs by conducting extensive experiments on various configurations such as architecture, dataset, optimization strategy, time constant, and timesteps. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical Engineering, Yale University, New Haven, CT, USA 2 Technology Innovation Institute, Abu Dhabi, UAE {youngeun.kim, yuhang.li, hyoungseob.park, yeshwanth.venkatesha, priya.panda}@yale.edu, Anna.Hambitzer@tii.ae |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present any structured pseudocode. |
| Open Source Code | Yes | Code is available at https://github.com/Intelligent-Computing Lab-Yale/Exploring-Temporal-Information-Dynamics-in Spiking-Neural-Networks. |
| Open Datasets | Yes | CIFAR10 (Krizhevsky and Hinton 2009), SVHN (Netzer et al. 2011), Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf 2017) and CIFAR100 (Krizhevsky and Hinton 2009). |
| Dataset Splits | No | The paper uses standard datasets like CIFAR10 and SVHN but does not explicitly provide specific train/validation/test split percentages, sample counts, or refer to a specific predefined split strategy within the paper. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for conducting experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions general software components like 'spatio-temporal back-propagation' and 'SGD optimizer' but does not provide specific version numbers for any libraries, frameworks (e.g., PyTorch version), or other ancillary software dependencies used for replication. |
| Experiment Setup | Yes | The default setting for all experiments is as follows: timestep 10, time constant 2, SGD optimizer with learning rate 3e-1, weight decay 5e-4, CIFAR10 dataset, and Res Net19 architecture. We use ϵ = 8 255 for FGSM attack, and [ ϵ = 8 255, α = 4 255, n = 10] for PGD attack. We select αcifar10=[1e-3, 1e-2, 7e-2], αsvhn=[1e-4, 1e-2, 7e-2], αcifar100=[1e-4, 1e-3, 1e-2], for [αlow, αintermediate, αhigh ]. |