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Description
  • 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.
  • 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. (en)
Title
  • Statistical approach in search for optimal signal in simple olfactory neuronal models
  • Statistical approach in search for optimal signal in simple olfactory neuronal models (en)
skos:prefLabel
  • Statistical approach in search for optimal signal in simple olfactory neuronal models
  • Statistical approach in search for optimal signal in simple olfactory neuronal models (en)
skos:notation
  • RIV/00216224:14310/08:00024207!RIV10-MSM-14310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET400110401), P(GD201/05/H007), P(LC06024), P(LC554), Z(AV0Z50110509)
http://linked.open...iv/cisloPeriodika
  • Issues 1-2
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 397294
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14310/08:00024207
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Fisher information; Olfactory neuron; Optimal signal (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • NL - Nizozemsko
http://linked.open...ontrolniKodProRIV
  • [A02CD3596CE4]
http://linked.open...i/riv/nazevZdroje
  • Mathematical Biosciences
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • Volume 214
http://linked.open...iv/tvurceVysledku
  • Lánský, Petr
  • Pokora, Ondřej
http://linked.open...ain/vavai/riv/wos
  • 000258743200015
http://linked.open...n/vavai/riv/zamer
issn
  • 0025-5564
number of pages
http://localhost/t...ganizacniJednotka
  • 14310
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