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@proceedings{877224,
author = {Pokora, Ondřej and Lánský, Petr},
booktitle = {18th Annual Computational Neuroscience Meeting CNS*2009},
keywords = {sensory neurons;Fisher information;lower bounds;input-output curve},
language = {eng},
title = {Optimal odor intensity in olfactory neuronal models},
url = {http://www.biomedcentral.com/content/files/pdf/1471-2202-10-S1-full.pdf},
year = {2009}
}
TY - CONF
ID - 877224
AU - Pokora, Ondřej - Lánský, Petr
PY - 2009
TI - Optimal odor intensity in olfactory neuronal models
KW - sensory neurons;Fisher information;lower bounds;input-output curve
UR - http://www.biomedcentral.com/content/files/pdf/1471-2202-10-S1-full.pdf
N2 - 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.
ER -
POKORA, Ondřej and Petr LÁNSKÝ. Optimal odor intensity in olfactory neuronal models. In \textit{18th Annual Computational Neuroscience Meeting CNS*2009}. 2009. ISSN~1471-2202.
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