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Statistical approach in search for optimal signal in simple olfactory neuronal models
O Pokora, P Lansky
Language English Country United States
- MeSH
- Algorithms MeSH
- Olfactory Receptor Neurons physiology MeSH
- Financing, Organized 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
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.
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- $a Statistical approach in search for optimal signal in simple olfactory neuronal models / $c O Pokora, P Lansky
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- $a Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Janackovo namesti 2a, 602 00 Brno, Czech Republic.
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- $a 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.
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