Collective dynamics and long-range order in thermal neuristor networks

Yuan-Hang Zhang (), Chesson Sipling, Erbin Qiu, Ivan K. Schuller and Massimiliano Di Ventra ()
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Yuan-Hang Zhang: University of California San Diego
Chesson Sipling: University of California San Diego
Erbin Qiu: University of California San Diego
Ivan K. Schuller: University of California San Diego
Massimiliano Di Ventra: University of California San Diego

Nature Communications, 2024, vol. 15, issue 1, 1-8

Abstract: 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 order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Date: 2024
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DOI: 10.1038/s41467-024-51254-4

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