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• W3134295666 abstract "Neurons exhibit $ephantom{rule{0}{0ex}}xphantom{rule{0}{0ex}}cphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}tphantom{rule{0}{0ex}}aphantom{rule{0}{0ex}}bphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}lphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}tphantom{rule{0}{0ex}}y$, the dynamical property that is likewise key to biologically inspired artificial intelligence. The neuromorphic circuits proposed so far have been slow and power-hungry. Seeking a better architecture that supports spikes as information carriers, the authors look to resonant tunneling diodes as excitable neuromorphic spike generators. These nonlinear quantum nanoelectronic elements can reach terahertz frequencies, and may be coupled to nanolasers for all-optical data transmission. This study theoretically characterizes their spiking and bursting dynamics, and may establish a basis for fast, minimal-power optoelectronic circuits for machine learning." @default.
• W3134295666 created "2021-03-15" @default.
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• W3134295666 date "2021-03-05" @default.
• W3134295666 modified "2023-10-02" @default.
• W3134295666 title "Bursting and Excitability in Neuromorphic Resonant Tunneling Diodes" @default.
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• W3134295666 doi "https://doi.org/10.1103/physrevapplied.15.034017" @default.
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