Optimal odor intensity in simple olfactory neuronal models

a 2009

Optimal odor intensity in simple olfactory neuronal models

POKORA, Ondřej and Petr LÁNSKÝ

Basic information

Original name

Optimal odor intensity in simple olfactory neuronal models

Authors

POKORA, Ondřej (203 Czech Republic, guarantor, belonging to the institution) and Petr LÁNSKÝ (203 Czech Republic, belonging to the institution)

Edition

Joint Conference of the Czech and Slovak Neuroscience Societies, 2009

Other information

Language

English

Type of outcome

Conference abstract

Field of Study

10101 Pure mathematics

Country of publisher

Czech Republic

Confidentiality degree

is not subject to a state or trade secret

RIV identification code

RIV/00216224:14310/09:00039597

Organization unit

Faculty of Science

Keywords in English

olfactory neuron;optimal stimulus

Changed: 13/1/2015 23:11, Mgr. Ondřej Pokora, Ph.D.

Abstract

In the original language

Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons. Three theoretical models and a realistic model for binding and activation of odorant in olfactory sensory neurons have been investigated. The models assume that the response, concentration of activated receptors, is determined by the signal, fixed log-concentration of odorant in perireceptor space. Dependency of the mean response on the signal is realized through the input-output function. How the concentration of activated receptors can code the intensity of odorant is analyzed using statistical properties of the steady-state responses. An approach, we use here, is based on stochastic variant of the 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 (concentration of the odorant) can be estimated from the response (concentration of activated receptors), the Fisher information and its approximations are used. The Fisher information is the inverse asymptotic variance of the best unbiased estimator of the signal, that means the higher the Fisher information is the better estimation of the corresponding signal can be achieved. These measures are computed and applied to locate the the odorant concentration which is most suitable for identification. Results are compared with the classical approach to determine the coding range via steepness of the input-output transfer function. The point in which the first derivative of the input-output function is maximal coincides with the point of maxima of the Fisher information in the simplest model. The obtained results differ in more complex models comprising the activation step(s). The study extends our previous results.

Links

LC06024, research and development project

Name: Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku

Investor: Ministry of Education, Youth and Sports of the CR, Jaroslav Hájek Center for Theoretical and Applied Statistics

Cite

POKORA, Ondřej and Petr LÁNSKÝ. Optimal odor intensity in simple olfactory neuronal models. In Joint Conference of the Czech and Slovak Neuroscience Societies. 2009.

@proceedings{877235,
   author = {Pokora, Ondřej and Lánský, Petr},
   booktitle = {Joint Conference of the Czech and Slovak Neuroscience Societies},
   keywords = {olfactory neuron;optimal stimulus},
   language = {eng},
   title = {Optimal odor intensity in simple olfactory neuronal models},
   year = {2009}
}

TY  - CONF
ID  - 877235
AU  - Pokora, Ondřej - Lánský, Petr
PY  - 2009
TI  - Optimal odor intensity in simple olfactory neuronal models
KW  - olfactory neuron;optimal stimulus
N2  - Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons. Three theoretical models and a realistic model for binding and activation of odorant in olfactory sensory neurons have been investigated. The models assume that the response, concentration of activated receptors, is determined by the signal, fixed log-concentration of odorant in perireceptor space. Dependency of the mean response on the signal is realized through the input-output function. How the concentration of activated receptors can code the intensity of odorant is analyzed using statistical properties of the steady-state responses. An approach, we use here, is based on stochastic variant of the 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 (concentration of the odorant) can be estimated from the response (concentration of activated receptors), the Fisher information and its approximations are used. The Fisher information is the inverse asymptotic variance of the best unbiased estimator of the signal, that means the higher the Fisher information is the better estimation of the corresponding signal can be achieved. These measures are computed and applied to locate the the odorant concentration which is most suitable for identification. Results are compared with the classical approach to determine the coding range via steepness of the input-output transfer function. The point in which the first derivative of the input-output function is maximal coincides with the point of maxima of the Fisher information in the simplest model. The obtained results differ in more complex models comprising the activation step(s). The study extends our previous results.
ER  -

POKORA, Ondřej and Petr LÁNSKÝ. Optimal odor intensity in simple olfactory neuronal models. In \textit{Joint Conference of the Czech and Slovak Neuroscience Societies}. 2009.

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