Models Developed for Spiking Neural Networks
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Abstract (arXiv)
Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared to the brain. Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. However, their application in real-world and complicated machine learning tasks were limited. Recently, they have shown great potential in solving such tasks. Due to their energy efficiency and temporal dynamics there are many promises in their future development. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems. Furthermore, the simple learning rules developed for SNNs, such as STDP and R-STDP, can be a potential alternative to replace the backpropagation algorithm used in DNNs.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 71 references that are in the map. First posted on arXiv 8 Dec 2022.
Builds on
22 of 71 references.
References · 22
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.