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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F09%3A00039594%21RIV10-MSM-14310___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
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 neuronal model. 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 function belonging 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. 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 neuronal model. 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 function belonging 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.
dcterms:title
Optimal odor intensity in olfactory neuronal models Optimal odor intensity in olfactory neuronal models
skos:prefLabel
Optimal odor intensity in olfactory neuronal models Optimal odor intensity in olfactory neuronal models
skos:notation
RIV/00216224:14310/09:00039594!RIV10-MSM-14310___
n3:aktivita
n13:P
n3:aktivity
P(LC06024)
n3:dodaniDat
n8:2010
n3:domaciTvurceVysledku
n6:2989387 n6:1350374
n3:druhVysledku
n16:O
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
331877
n3:idVysledku
RIV/00216224:14310/09:00039594
n3:jazykVysledku
n15:eng
n3:klicovaSlova
sensory neurons; Fisher information; lower bounds; input-output curve
n3:klicoveSlovo
n12:sensory%20neurons n12:Fisher%20information n12:input-output%20curve n12:lower%20bounds
n3:kontrolniKodProRIV
[49BC6955843B]
n3:obor
n10:BA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n11:LC06024
n3:rokUplatneniVysledku
n8:2009
n3:tvurceVysledku
Pokora, Ondřej Lánský, Petr
n14:organizacniJednotka
14310