Spiking neural networks (SNNs) offer promise for energy-efficient AI applications by encoding information in firing times. They can realize both continuous and discontinuous functions, unlike ReLU networks. Complexity bounds are provided for LSNNs to emulate multi-layer ANNs. LSNNs exhibit distinct characteristics from ReLU-ANNs, making them suitable for approximating discontinuous functions efficiently. The number of linear regions generated by LSNNs scales exponentially with input dimension, offering expressivity comparable to deep ReLU networks.
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by Manjot Singh... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2308.08218.pdfDeeper Inquiries