Stochastic transition in synchronized spiking nanooscillators.

Autor:	Qiu E; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.; Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA 92093., Salev P; Department of Physics and Astronomy, University of Denver, Denver, CO 80208., Torres F; Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago 7800024, Chile., Navarro H; Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA 92093., Dynes RC; Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA 92093., Schuller IK; Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA 92093.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2023 Sep 19; Vol. 120 (38), pp. e2303765120. Date of Electronic Publication: 2023 Sep 11.
DOI:	10.1073/pnas.2303765120
Abstrakt:	This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.
Databáze:	MEDLINE
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