Evidence map›Paper›PMID 34433669›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Low-temperature emergent neuromorphic networks with correlated oxide devices.

Uday S Goteti, Ivan A Zaluzhnyy, Shriram Ramanathan, Robert C Dynes, Alex Frano

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Uday S GotetiDepartment of Physics, University of California San Diego, La Jolla, CA 92093.ORCID 0000-0002-9430-451X
Ivan A ZaluzhnyyDepartment of Physics, University of California San Diego, La Jolla, CA 92093.
Shriram RamanathanSchool of Materials Engineering, Purdue University, West Lafayette, IN 47907.
Robert C DynesDepartment of Physics, University of California San Diego, La Jolla, CA 92093; rdynes@ucsd.edu afrano@ucsd.edu.ORCID 0000-0001-6740-9677
Alex FranoDepartment of Physics, University of California San Diego, La Jolla, CA 92093; rdynes@ucsd.edu afrano@ucsd.edu.ORCID 0000-0001-9316-129X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuromorphic computing-which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, and dendrites-offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

Indexed as

emergent phenomenahardware neural networksneuromorphic computingstrongly correlated systems

Identifiers

PMID34433669
PMCPMC8536335

What OpenQuestion holds

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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.