Evidence map›Paper›PMID 37624895›Full record

ArticleScience advances2023

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost.

Tielin Zhang, Xiang Cheng, Shuncheng Jia, Chengyu T Li, Mu-Ming Poo, Bo Xu

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2023. 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
4.0field-weighted citation impact, top 5% of its field
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, 31 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Tielin ZhangInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID 0000-0002-5111-9891
Xiang ChengInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID 0000-0003-3246-7062
Shuncheng JiaInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID 0000-0002-5165-9652
Chengyu T LiShanghai Center for Brain Science and Brain-inspired Technology, Lingang Laboratory, Shanghai 200031, China.ORCID 0000-0001-6829-0209
Mu-Ming PooShanghai Center for Brain Science and Brain-inspired Technology, Lingang Laboratory, Shanghai 200031, China.
Bo XuInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID 0000-0002-1111-1529
Chinese Academy of Sciences · CNCenter for Excellence in Brain Science and Intelligence Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Indexed as

AlgorithmsNeural Networks, ComputerBrainLearningRecognition, Psychology

Identifiers

PMID37624895
PMCPMC10456855
OpenAlexW4386157910

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.