This repository serves as a curated collection of research papers and associated code for Spiking Neural Networks (SNNs), targeting researchers and practitioners in the field. It aims to consolidate state-of-the-art advancements in SNNs, providing a valuable resource for understanding and implementing SNN-based solutions.

How It Works

The repository organizes papers by major AI conferences and journals, spanning from 2018 to the present. Each entry typically includes a link to the paper and, where available, a link to the corresponding code implementation. This structure facilitates easy navigation and access to relevant research for specific years or conferences.

Quick Start & Requirements

• Installation: No direct installation is required as this is a curated list of papers and code. Users will need to follow the instructions within each linked code repository for setup.
• Prerequisites: Specific code repositories may require Python, deep learning frameworks (e.g., PyTorch, TensorFlow), and potentially specialized hardware like GPUs, depending on the complexity of the SNN implementation.
• Resources: Accessing the papers requires an internet connection. Running the code will depend on the computational requirements of each individual project.
• Links:
  • Awesome SNN Paper Collection (2018-2021)
  • 2022 Top Conferences and Journals SNN Papers

Highlighted Details

• Comprehensive coverage of SNN research from major conferences (CVPR, NeurIPS, ICLR, ICML, etc.) and journals.
• Inclusion of code links for a significant portion of the listed papers, enabling practical implementation.
• Continuous updates to incorporate the latest advancements in the SNN field.
• Categorization by year and conference for efficient browsing.

Maintenance & Community

The project is actively maintained and welcomes community contributions via pull requests for new papers. Collaboration is encouraged.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The licensing and compatibility of the individual papers and code repositories will vary and must be checked on a per-item basis.

Limitations & Caveats

The repository is a curated list and does not provide a unified SNN framework or library. Users must independently manage dependencies and execution environments for each linked code project. The availability and quality of linked code can vary.