Memristor networks for real-time neural activity analysis
Designing energy efficient artificial neural networks for real-time analysis remains a challenge. Here, the authors report the development of a perovskite halide (CsPbI3) memristor-based Reservoir Computing system for real-time recognition of neural firing patterns and neural synchronization states.
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-16261-1 |
| Summary: | Designing energy efficient artificial neural networks for real-time analysis remains a challenge. Here, the authors report the development of a perovskite halide (CsPbI3) memristor-based Reservoir Computing system for real-time recognition of neural firing patterns and neural synchronization states. |
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| ISSN: | 2041-1723 |