ArticleScientific reports2015
An odor detection system based on automatically trained mice by relative go no-go olfactory operant conditioning.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Complex Sound Discrimination in Zebrafish: Auditory Learning Within a Novel "Go/Go" Decision-Making Paradigm.Animals : an open access journal from MDPI · 2025Article
- Article
- Multi-Odor Discrimination by Rat Sniffing for Potential Monitoring of Lung Cancer and Diabetes.Sensors (Basel, Switzerland) · 2021Article
- Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- An automated homecage system for multiwhisker detection and discrimination learning in mice.PloS one · 2020Article
- Olfactory Training Prevents Olfactory Dysfunction Induced by Bulbar Excitotoxic Lesions: Role of Neurogenesis and Dopaminergic Interneurons.Molecular neurobiology · 2019Article
- Development of a Low-cost, Comprehensive Recording System for Circadian Rhythm Behavior.Experimental neurobiology · 2018Article
- Training Rats Using Water Rewards Without Water Restriction.Frontiers in behavioral neuroscience · 2018Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Odor detection applications are needed by human societies in various circumstances. Rodent offers unique advantages in developing biologic odor detection systems. This report outlines a novel apparatus designed to train maximum 5 mice automatically to detect odors using a new olfactory, relative go no-go, operant conditioning paradigm. The new paradigm offers the chance to measure real-time reliability of individual animal's detection behavior with changing responses. All of 15 water-deprivation mice were able to learn to respond to unpredictable delivering of the target odor with higher touch frequencies via a touch sensor. The mice were continually trained with decreasing concentrations of the target odor (n-butanol), the average correct percent significantly dropped when training at 0.01% solution concentration; the alarm algorithm showed excellent recognition of odor detection behavior of qualified mice group through training. Then, the alarm algorithm was repeatedly tested against simulated scenario for 4 blocks. The mice acted comparable to the training period during the tests, and provided total of 58 warnings for the target odor out of 59 random deliveries and 0 false alarm. The results suggest this odor detection method is promising for further development in respect to various types of odor detection applications.
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