Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Coding of odor intensity

P Lánský1, J P Rospars

  • 1Institute of Physiology, Academy of Sciences of Czech Republic, Prague.

Bio Systems
|January 1, 1993
PubMed
Summary
This summary is machine-generated.

This study models how olfactory systems code odor intensity. It explains neuronal responses to odorants, predicting firing frequencies that align with experimental data.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties.

Chaos (Woodbury, N.Y.)·2020
Same author

Entropy factor for randomness quantification in neuronal data.

Neural networks : the official journal of the International Neural Network Society·2017
Same author

Parametric inference of neuronal response latency in presence of a background signal.

Bio Systems·2013
Same author

Randomness of spontaneous activity and information transfer in neurons.

Physiological research·2008
Same author

Modeling the influence of non-adherence on antibiotic efficacy: application to ciprofloxacin.

International journal of clinical pharmacology and therapeutics·2007
Same author

Different types of noise in leaky integrate-and-fire model of neuronal dynamics with discrete periodical input.

General physiology and biophysics·2004
Same journal

Ruliological Resilience: Pattern Restoration and Robustness in Wolfram Patterns. A Basis for Regeneration, Not Just in Cone Shells?

Bio Systems·2026
Same journal

The quantum-to-classical transducer: A thermodynamic and quantum mechanical framework for the emergence of bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
See all related articles

Area of Science:

  • Neuroscience
  • Computational Biology
  • Olfactory System Modeling

Background:

  • Odor intensity coding is crucial for olfactory perception.
  • Existing models do not fully capture the complex neuronal dynamics in the olfactory system.

Purpose of the Study:

  • To propose a computational model for odor intensity coding in the first two neuronal layers of the olfactory system.
  • To describe the stochastic firing activity of olfactory neurons at varying odorant concentrations.

Main Methods:

  • Modeling receptor occupation and activation as birth-death processes dependent on odorant concentration and type.
  • Simulating first-order neuron spike generation using a generator potential and a time-decaying threshold.
  • Integrating signals (excitation, lateral inhibition, self-inhibition) at second-order neurons using a first passage time scheme.

Related Experiment Videos

Main Results:

  • The model predicts distinct stochastic firing regimes for first-order neurons at different odorant concentrations.
  • Second-order neuron activity integrates complex inputs, including lateral inhibition.
  • Predicted mean firing frequencies as a function of stimulus concentration match experimental observations for both neuron types.

Conclusions:

  • The proposed model provides a comprehensive framework for understanding odor intensity coding in the olfactory system.
  • The model successfully replicates key experimental findings on neuronal firing patterns.
  • This work offers insights into the computational principles underlying olfactory processing.