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• W4317383371 abstract "Spiking neural networks (SNNs), known as third-generation neural networks, are practical tools for processing complex spatiotemporal information. However, the lack of clarity on how biological neurons encode information via impulse signals and the lack of neuronal models and recognized efficient training algorithms that combine low implementation cost, and high biological interpretation have left the SNNs behind second-generation neural networks in most learning tasks. This paper describes the main research areas in SNNs, including neuronal models, coding methods, network architectures, and learning algorithms, and discusses in detail each perspective's current state of the arts, and reviews future research directions in the field." @default.
• W4317383371 created "2023-01-19" @default.
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• W4317383371 date "2022-12-05" @default.
• W4317383371 modified "2023-10-01" @default.
• W4317383371 title "The development of Spiking Neural Network: A Review" @default.
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• W4317383371 doi "https://doi.org/10.1109/robio55434.2022.10012028" @default.
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