Abstract
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.
Highlights
Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Don H Johnson Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf
Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons
As a measure how well the signal can be estimated from the response, the Fisher information and its lower bounds are used [3]
Summary
Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Don H Johnson Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf . Email: Ondrej Pokora* - pokora@math.muni.cz * Corresponding author from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. Published: 13 July 2009 BMC Neuroscience 2009, 10(Suppl 1):P118 doi:10.1186/1471-2202-10-S1-P118 Signal processing in olfactory systems is initiated by binding of odorant molecules to receptor molecules embedded in the membranes of sensory neurons.
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