An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation
Recurrent spiking neural networks have garnered interest due to their energy efficiency; however, they suffer from lower accuracy compared to conventional neural networks. Here, the authors present an alternative neuron model and its efficient hardware implementation, demonstrating high classificati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-24427-8 |
| Summary: | Recurrent spiking neural networks have garnered interest due to their energy efficiency; however, they suffer from lower accuracy compared to conventional neural networks. Here, the authors present an alternative neuron model and its efficient hardware implementation, demonstrating high classification accuracy across a range of datasets. |
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| ISSN: | 2041-1723 |