Neural Comput December 2021 PMID 34758479

Confidence-Controlled Hebbian Learning Efficiently Extracts Category Membership From Stimuli Encoded in View of a Categorization Task.

Kevin Berlemont, Jean-Pierre Nadal

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Mentions

Peak: 3 in Aug 2020

across 2 sources since Aug 2020

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200,698

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123

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Aug 2020 – Jan 2022

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X / Twitter5 posts
YouTube2 videos

AI synopsis

What people are saying

Discussions highlight the article's innovative confidence-controlled Hebbian learning, with mentions of it efficiently extracting category information, which shows users find the research meaningful and relevant. Words like 'congrats' and hashtags indicate admiration and curiosity, while references to the method's efficiency and learning process convey moderate engagement and recognition of potential significance.

AgreementModerate agreement

Most discussions express positive recognition of the article’s contributions, with some explicitly congratulating the authors, indicating general support.

InterestHigh interest

The high interest score reflects enthusiastic reactions and emphasis on the topic, suggesting that viewers find the research compelling and relevant.

EngagementModerate engagement

The engagement level is moderate, with some users praising the method or the significance of the findings, but few dive into detailed critique or discussion.

ImpactModerate impact

While the posts acknowledge the importance of the approach, they mainly recognize its potential rather than evidence of profound immediate impact or broad application.

Abstract in brief

The study investigates how decision-making neural networks learn to categorize stimuli, highlighting that traditional reward-based Hebbian learning (RMHL) is ineffective when the sensory coding layer is optimized for the task.
Instead, the authors propose a confidence-controlled Hebbian learning rule, which uses the neural network's confidence in its decisions—derived from its nonlinear dynamics—to guide learning, leading to more efficient extraction of category information.
This confidence-based approach is local, does not require tracking past rewards, and achieves near-optimal performance by approximating a gradient descent method on a reward-maximization function.]

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All mentions

Confidence-Controlled Hebbian Learning for Efficient Categorization

Neuromatch Conference · Nov 29, 2021 83 views

Neuromatch 4 - Learning a categorization task through confidence-controlled Hebbian learning

Kevin Berlemont · Nov 19, 2021 41 views

Kevin Berlemont

@BerlemontKevin · X

RT @PslQlife: Congrats to Jean-Pierre Nadal and @BerlemontKevin

Jan 10, 2022 · 1 share · 85 followers View post →

PSL-Qlife

@PslQlife · X

Congrats to Jean-Pierre Nadal and @BerlemontKevin

Jan 10, 2022 · 1 like · 1 share · 628 followers View post →

Open Neuroscience

@openneurosci · X

RT @biorxiv_neursci: Confidence-controlled Hebbian learning efficiently extracts category membership from stimuli encoded in view of a cate…

Aug 7, 2020 · 1 share · 2,371 followers View post →

bioRxiv Neuroscience

@biorxiv_neursci · X

Confidence-controlled Hebbian learning efficiently extracts category membership from stimuli encoded in view of a categorization task https://t.co/38CV03m3CU #biorxiv_neursci

Aug 7, 2020 · 1 share · 60,069 followers View post →

bioRxiv

@biorxivpreprint · X

Confidence-controlled Hebbian learning efficiently extracts category membership from stimuli encoded in view of a categorization task https://t.co/zBGMFkvfac #bioRxiv

Aug 7, 2020 · 137,415 followers View post →