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Related Concept Videos

Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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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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Olfaction01:25

Olfaction

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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...
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Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

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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...
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Related Experiment Video

Updated: Oct 1, 2025

Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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Developmental and evolutionary constraints on olfactory circuit selection.

Naoki Hiratani1, Peter E Latham1

  • 1Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|March 9, 2022
PubMed
Summary
This summary is machine-generated.

Biological neural networks evolve their architecture for optimal learning. This study analyzes olfactory circuits, finding genome and longevity constrain hidden-layer size, aligning with observed scaling in mammals and insects.

Keywords:
model selectionneural circuitolfactionstatistical learning theory

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Evolutionary Biology

Background:

  • Biological neural networks, particularly early olfactory circuits in mammals and insects, exhibit significant diversity in size despite structural similarities.
  • Understanding the evolutionary pressures that shape neural network architecture is crucial for both neuroscience and artificial intelligence.

Purpose of the Study:

  • To test the hypothesis that biological neural networks optimize their architecture through evolution for enhanced learning capabilities.
  • To analytically determine the scaling relationship between optimal hidden-layer size and input-layer size in simplified neural network models.

Main Methods:

  • Approximating early olfactory circuits of mammals and insects as three-layer neural networks.
  • Analytically estimating the scaling of the optimal hidden-layer size with respect to the input-layer size.

Main Results:

  • Both organism longevity and the amount of information encoded in the genome act as constraints on the hidden-layer size.
  • The analysis reveals a range of possible allometric scalings for the hidden-layer size.
  • Experimentally observed allometric scalings in mammals and insects are consistent with the biologically plausible values derived from the model.

Conclusions:

  • Evolutionary constraints, including longevity and genomic information, play a significant role in determining neural network architecture.
  • The findings support the hypothesis that biological neural networks are optimized for learning.
  • This research provides insights applicable to understanding both biological and artificial neural networks.