Statistical approach in search for optimal signal in simple olfactory neuronal models
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
18400236
DOI
10.1016/j.mbs.2008.02.010
PII: S0025-5564(08)00040-0
Knihovny.cz E-resources
- MeSH
- Algorithms MeSH
- Olfactory Receptor Neurons physiology MeSH
- Kinetics MeSH
- Humans MeSH
- Models, Neurological * MeSH
- Odorants MeSH
- Receptors, Odorant physiology MeSH
- Signal Transduction * MeSH
- Models, Statistical MeSH
- Stochastic Processes MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Receptors, Odorant MeSH
Several models (concentration detectors and a flux detector) for coding of odor intensity in olfactory sensory neurons are investigated. Behavior of the system is described by different stochastic processes of binding the odorant molecules to the receptors and their activation. Characteristics how well the odorant concentration can be estimated from the knowledge of response, the number of activated neurons, are studied. The approach is based on the Fisher information and analogous measures. These measures of optimality are computed and applied to locate the odorant concentration which is most suitable for coding. The results are compared with the classical deterministic approach which judges the optimal odorant concentration via steepness of the input-output function.
References provided by Crossref.org
Ergodicity and parameter estimates in auditory neural circuits
