{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["18"],"submitter":["Zhang H"],"pubmed_abstract":["<h4>Introduction</h4>Spiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.<h4>Methods</h4>Recognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of in"],"journal":["Frontiers in neuroscience"],"pagination":["1371290"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10977074"],"repository":["biostudies-literature"],"pubmed_title":["SGLFormer: Spiking Global-Local-Fusion Transformer with high performance."],"pmcid":["PMC10977074"],"pubmed_authors":["Yu L","Fan X","Zhou C","Zhang H","Tian Y","Ma Z","Huang L","Zhou H"],"additional_accession":[]},"is_claimable":false,"name":"SGLFormer: Spiking Global-Local-Fusion Transformer with high performance.","description":"<h4>Introduction</h4>Spiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.<h4>Methods</h4>Recognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of in","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-22T21:33:55.211Z","creation":"2025-04-06T03:34:45.781Z"},"accession":"S-EPMC10977074","cross_references":{"pubmed":["38550564"],"doi":["10.3389/fnins.2024.1371290"]}}