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Olfaction

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

Olfactory Receptors: Location and Structure

Olfactory Receptors: Location and Structure

The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...

Physiology of Smell and Olfactory Pathway

Physiology of Smell and Olfactory Pathway

Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...

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Related Experiment Video

Updated: Jul 4, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Data-centric artificial olfactory system based on the eigengraph.

Seung-Hyun Sung^1,2, Jun Min Suh^3,4, Yun Ji Hwang¹

¹School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea.

Nature Communications

|February 8, 2024

Summary

This summary is machine-generated.

This study introduces a data-centric approach for artificial olfactory systems using Eigengraphs and Mel-Frequency Cepstral Coefficients. This method enhances odor identification by reducing data waste and improving artificial intelligence analysis for gas molecules.

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Last Updated: Jul 4, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

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Olfactory Context Dependent Memory: Direct Presentation of Odorants

Olfactory Context Dependent Memory: Direct Presentation of Odorants

Published on: September 18, 2018

Area of Science:

Electrochemistry
Artificial Intelligence
Sensory Systems

Background:

Electronic nose systems often lose critical data during odor identification.
Current sensitivity-focused data methods hinder deep analysis of gas molecule attributes.

Purpose of the Study:

To develop a data-centric approach for standardized artificial olfactory systems.
To leverage Eigengraphs and Mel-Frequency Cepstral Coefficients for improved gas classification.

Main Methods:

Formalized the concept of Eigengraphs in electrochemistry.
Substantialized odor attributes using Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors.
Applied deep learning for gas classification experiments.

Main Results:

Demonstrated effectiveness and applicability of the Eigengraph-based approach.
Successfully classified complex mixed gases and automobile exhaust gases.
Reduced data waste in odor identification processes.

Conclusions:

The proposed data-centric method enables standardized artificial olfactory systems.
Findings can advance artificial olfactory technology and deep learning applications.
The approach is inspired by human olfactory mechanisms.