Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions
We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirabl...
Guardado en:
Autores principales: | Ran Cheng, Uday S. Goteti, Harrison Walker, Keith M. Krause, Luke Oeding, Michael C. Hamilton |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/25254a0173654ec58360e1fbd29174a6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Dynamics of coupled sine–Gordon equations: Inductively stacked long Josephson junctions with heterogeneous drives
por: Wajahat Ali Khan, et al.
Publicado: (2021) -
Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing
por: Margot Wagner, et al.
Publicado: (2021) -
Morphometric and Morphological Analysis of Neuromuscular Junction Alterations in the Denervated Rat Diaphragm
por: Torrejais,M. M, et al.
Publicado: (2009) -
Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review
por: Yann Beilliard, et al.
Publicado: (2021) -
Pure Graphene Oxide Vertical p–n Junction with Remarkable Rectification Effect
por: Yan Fan, et al.
Publicado: (2021)