Evidence map›Paper›PMID 35170246›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Flexible Neural Network Realized by the Probabilistic SiO

Sanghyeon Choi, Jingon Jang, Min Seob Kim, Nam Dong Kim, Jeehyun Kwag, Gunuk Wang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Flexible Neural Network Realized by the Probabilistic SiOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Article
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

6 authors.

Sanghyeon ChoiKU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Jingon JangKU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Min Seob KimInstitute of Advanced Composite Materials, Korea Institute of Science and Technology, 92 Chudong-ro, Bongdong-eup, Wanju-gun, Jeollabuk-do, 55324, Republic of Korea.
Nam Dong KimInstitute of Advanced Composite Materials, Korea Institute of Science and Technology, 92 Chudong-ro, Bongdong-eup, Wanju-gun, Jeollabuk-do, 55324, Republic of Korea.
Jeehyun KwagDepartment of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
Gunuk WangKU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.ORCID 0000-0001-6059-0530

Funding

Korea University Future ResearchKU-KIST Graduate School Program of Korea UniversityNational Research Foundation of KoreaNRF 2019R1A2C2003704NRF 2020M3F3A2A03082825NRF 2020R1A2C201423512NRF 2022M3H4A1A01009526
6 · The paper itself

Abstract

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiO

Indexed as

Neural Networks, ComputerSynapsesAlgorithmsHumansLearningPhysical Phenomenabarristordrop-connected networkneuromorphic computingprobabilistic synapsesiliconsilicon oxide

Identifiers

PMID35170246
PMCPMC9009121

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.