Optimal odor intensity in olfactory neuronal models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F09%3A00039594" target="_blank" >RIV/00216224:14310/09:00039594 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimal odor intensity in olfactory neuronal models

Popis výsledku v původním jazyce

Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons. An approach, which we use here, is based on stochastic variant ofthe law of mass action as a neuronalmodel. A model experiment is considered, in which a fixed odorant concentration is applied several times and realizations of steady-state characteristics are observed. The response is assumed to be a random variable with some probability density functionbelonging to a parametric family with the signal as a parameter. As a measure how well the signal can be estimated from the response, the Fisher information and its lower bounds are used. Another optimality measures are based on the theory of information, especially conditional and unconditional differential entropy. The study extends our previous results.

Název v anglickém jazyce

Optimal odor intensity in olfactory neuronal models

Popis výsledku anglicky

Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons. An approach, which we use here, is based on stochastic variant ofthe law of mass action as a neuronalmodel. A model experiment is considered, in which a fixed odorant concentration is applied several times and realizations of steady-state characteristics are observed. The response is assumed to be a random variable with some probability density functionbelonging to a parametric family with the signal as a parameter. As a measure how well the signal can be estimated from the response, the Fisher information and its lower bounds are used. Another optimality measures are based on the theory of information, especially conditional and unconditional differential entropy. The study extends our previous results.

Druh

O - Ostatní výsledky

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/LC06024" target="_blank" >LC06024: Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů