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An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation
We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to reali...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270926/ https://www.ncbi.nlm.nih.gov/pubmed/34244491 http://dx.doi.org/10.1038/s41467-021-24427-8 |