Evidence map›Paper›PMID 36465966›Full record

ArticleFrontiers in computational neuroscience2022

Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.

Jangho Lee, Jeonghee Jo, Byounghwa Lee, Jung-Hoon Lee, Sungroh Yoon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in computational neuroscience, 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. 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

5 authors.

Jangho LeeDepartment of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
Jeonghee JoInstitute of New Media and Communications, Seoul National University, Seoul, South Korea.
Byounghwa LeeCybreBrain Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
Jung-Hoon LeeCybreBrain Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
Sungroh YoonDepartment of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as

Indexed as

backpropagationbiologically plausible learningbrain-inspired learningdeep learningpredictive coding

Identifiers

PMID36465966
PMCPMC9709416

What OpenQuestion holds

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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.