Evidence map›Paper›PMID 42754585›Full record

ArticleNature communications2026

Intelligent artificial olfactory nervous system with sensory adaptation capabilities.

Gyuweon Jung, Jaehyeon Kim, Kangwook Choi, Hunhee Shin, Kwon Dongseok, Wonjun Shin, Jinwoo Park, Donghee Kim, Jangsaeng Kim, Ryun-Han Koo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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

12 authors.

Gyuweon JungInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0003-0357-0933
Jaehyeon KimInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Kangwook ChoiInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Hunhee ShinInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Kwon DongseokDepartment of Semiconductor Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.ORCID http://orcid.org/0000-0001-7676-8938
Wonjun ShinDepartment of Semiconductor Convergence Engineering & Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
Jinwoo ParkInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.ORCID http://orcid.org/0009-0008-2784-6301
Donghee KimInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Jangsaeng KimDepartment of System Semiconductor Engineering, Sogang University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0003-4519-135X
Ryun-Han KooInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0002-5708-3308
Woo Young ChoiInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Jong-Ho LeeInter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. jhl@snu.ac.kr.ORCID http://orcid.org/0000-0003-3559-9802

Funding

National Research Foundation of Korea (NRF) RS-2025-00517999
6 · The paper itself

Abstract

Accurate gas identification across diverse environments is crucial for the advancement of intelligent artificial olfaction, but remains challenging for conventional data-driven learning approaches. Here, we report an artificial olfactory nervous system capable of reliably identifying novel gas stimuli regardless of environmental variations by leveraging sensory adaptation mechanisms inspired by biological olfactory systems. Inspired by biological olfactory systems, our system effectively integrates artificial olfactory receptor neurons, cortical neurons, and intermediate olfactory neurons that facilitate signal transmission. Receptor neurons can dynamically adapt their sensitivity to olfactory stimuli, enabling rapid responses to familiar gases and selective filtering of background interference, thus generating distinctive signal patterns for novel gases. Signals from receptor neurons are modulated in olfactory neurons, then fed into the cortical neurons and translated into olfactory perception. We show that, despite being trained in limited settings, the system can accurately identify gases in various environments.

Indexed as

Adaptation, PhysiologicalOlfactory PerceptionOlfactory Receptor NeuronsSmellAnimalsGasesOdorantsGases

Identifiers

PMID42754585
PMCPMC13586236

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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.