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...

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Detalles Bibliográficos
Autores principales: Shaban, Ahmed, Bezugam, Sai Sukruth, Suri, Manan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
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

Internet

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

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