Statistical approach in search for optimal signal in simple olfactory neuronal models.
Several models (concentration detectors and a flux detector) for coding of odor intensity in olfactory sensory neurons are investigated. Behavior of the system is described by different stochastic processes of binding the odorant molecules to the receptors and their activation. Characteristics how well the odorant concentration can be estimated from the knowledge of response, the number of activated neurons, are studied. The approach is based on the Fisher information and analogous measures. These measures of optimality are computed and applied to locate the odorant concentration which is most suitable for coding. The results are compared with the classical deterministic approach which judges the optimal odorant concentration via steepness of the input-output function.
Pokora
Ondrej
O
Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Janáckovo námestí 2a, 602 00 Brno, Czech Republic.
Lánský
Petr
P
D000465:Algorithms; D000818:Animals; D006801:Humans; D007700:Kinetics; D008959:Models, Neurological; D015233:Models, Statistical; D009812:Odorants; D018034:Olfactory Receptor Neurons / Q000502:physiology*; D018035:Receptors, Odorant / Q000502:physiology*; D015398:Signal Transduction; D013269:Stochastic Processes
D018035:Receptors, Odorant