Collective dynamics and long-range order in thermal neuristor networks
Abstract: In the pursuit of scalable and energy-efficient neuromorphic devices, recent
research has unveiled a novel category of spiking oscillators, termed “thermal
neuristors.” These devices function via thermal interactions among neighboring
vanadium dioxide resistive memories, emulating biological neuronal behavior.
Here, we show that the collective dynamical behavior of networks of these
neurons showcases a rich phase structure, tunable by adjusting the thermal
coupling and input voltage. Notably, we identify phases exhibiting long-range
orderthat,however,doesnotarisefromcriticality,butratherfromthetimenon
local response of the system. In addition, we show that these thermal neuristor
arrays achieve high accuracy in image recognition and time series prediction
throughreservoir computing,withoutleveraginglong-rangeorder.Ourfindings
highlight a crucial aspect of neuromorphic computing with possible implica
tions on the functioning of the brain: criticality may not be necessary for the
efficient performance of neuromorphic systems in certain computational tasks.
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