ArticleNature communications2024
Collective dynamics and long-range order in thermal neuristor networks.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Computation embodied in population geometry.Nature communications · 2026Article
- Explicit electrothermal LLP VOScientific reports · 2026Article
- Electroforming in VOACS nano · 2026Article
- Protonic nickelate device networks for spatiotemporal neuromorphic computing.Nature nanotechnology · 2026Article
- Hydrogenated perovskite for brain-inspired computing.Nature nanotechnology · 2026Article
- Near field optical visualization of the nanoscale phase percolation dynamics of a VONature communications · 2026Article
- Collective dynamics and long-range order in thermal neuristor networks.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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