Evidence map›Paper›PMID 34188085›Full record

ArticleNature communications2021

Avalanches and edge-of-chaos learning in neuromorphic nanowire networks.

Joel Hochstetter, Ruomin Zhu, Alon Loeffler, Adrian Diaz-Alvarez, Tomonobu Nakayama, Zdenka Kuncic

Erratum issuedAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

  1. Diffusive memristors in the edge of chaos.Nature communications · 2026
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  11. Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Joel HochstetterSchool of Physics, University of Sydney, Sydney, NSW, Australia. joel.hochstetter@sydney.edu.au.ORCID http://orcid.org/0000-0003-3460-7305
Ruomin ZhuSchool of Physics, University of Sydney, Sydney, NSW, Australia.
Alon LoefflerSchool of Physics, University of Sydney, Sydney, NSW, Australia.
Adrian Diaz-AlvarezInternational Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
Tomonobu NakayamaSchool of Physics, University of Sydney, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0001-9696-475X
Zdenka KuncicSchool of Physics, University of Sydney, Sydney, NSW, Australia. zdenka.kuncic@sydney.edu.au.ORCID http://orcid.org/0000-0001-6765-3215

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Identifiers

PMID34188085
PMCPMC8242064

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.