Publication details
Optimal odor intensity in simple olfactory neuronal models
| Authors | |
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| Year of publication | 2009 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | 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. |
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