Evidence map›Paper›PMID 42741410›Full record

ArticleFrontiers in artificial intelligence2026

NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification.

Fabrice Vaussenat, Abhiroop Bhattacharya, Julie Payette, Thibaut Desmettre, Alireza Saidi, Ghyslain Gagnon, Sylvain G Cloutier

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Fabrice VaussenatDepartment of Electrical Engineering, École de technologie supérieure, Montréal, QC, Canada.
Abhiroop BhattacharyaDepartment of Electrical Engineering, École de technologie supérieure, Montréal, QC, Canada.
Julie PayetteDepartment of Electrical Engineering, École de technologie supérieure, Montréal, QC, Canada.
Thibaut DesmettreDepartment of Acute Medicine, Hôpital Universitaire de Genève, Genève, Switzerland.
Alireza SaidiInstitut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montréal, QC, Canada.
Ghyslain GagnonDepartment of Electrical Engineering, École de technologie supérieure, Montréal, QC, Canada.
Sylvain G CloutierDepartment of Electrical Engineering, École de technologie supérieure, Montréal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep neural networks for medical image classification rely almost exclusively on fixed activation functions such as ReLU. We introduce NeuroPlast, a parametric activation function whose four differentiable components (a shifted sigmoid modeling NMDA-type voltage gating, a Gaussian plateau inspired by AMPA receptor scaling, an excitatory rectifier, and an inhibitory leak) are combined through six learnable parameters. Two mixing strategies are evaluated: a

Indexed as

bio-inspired activation functionconvolutional neural networkdeep learningfrom-scratch trainingknowledge distillationlearnable activationmedical image classificationmetaplasticity

Identifiers

PMID42741410
PMCPMC13572696

What OpenQuestion holds

Textmetadata
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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.