This repository serves as a curated, daily updated list of research papers focused on Spiking Neural Networks (SNNs). It aims to provide researchers and practitioners in computational neuroscience, artificial intelligence, and neuromorphic engineering with a centralized resource for the latest advancements in SNNs.

How It Works

The project automatically scrapes and compiles new arXiv papers related to Spiking Neural Networks, organizing them into a table. This table includes publication date, title, authors, and links to the paper's PDF and associated code (if available). This automated process ensures a consistently updated and easily accessible collection of relevant research.

Quick Start & Requirements

This project is a data compilation and does not have direct installation or execution requirements. It serves as a reference list.

Highlighted Details

• Daily Updates: Provides the most current research in SNNs.
• Comprehensive Data: Includes essential metadata for each paper.
• Code Availability: Links to code repositories facilitate reproducibility and further research.
• Broad Scope: Covers diverse applications and theoretical aspects of SNNs.

Maintenance & Community

This is a community-driven project, with updates likely driven by automated processes and contributions from the research community.

Licensing & Compatibility

The project itself is a list of links to external research papers. The licensing of the papers themselves varies by publisher and author.

Limitations & Caveats

The project's utility is limited to being a reference list; it does not provide tools for SNN implementation or analysis. Code availability is dependent on the authors of the linked papers.