  On the basis of the evident ability of neuronal olfactory systems to evaluate
the intensity of an odorous stimulus and at the same time also recognise the
identity of the odorant over a large range of concentrations, a few
biologically-realistic hypotheses on some of the underlying neural processes
are made. In particular, it is assumed that the receptor neurons mean
firing-rate scale monotonically with odorant intensity, and that the receptor
sensitivities range widely across odorants and receptor neurons hence leading
to highly distributed representations of the stimuli. The mathematical
implementation of the phenomenological postulates allows for inferring explicit
functional relationships between some measurable quantities. It results that
both the dependence of the mean firing-rate on odorant concentration and the
statistical distribution of receptor sensitivity across the neuronal population
are power-laws, whose respective exponents are in an arithmetic, testable
relationship.
  In order to test quantitatively the prediction of power-law dependence of
population mean firing-rate on odorant concentration, a probabilistic model is
created to extract information from data available in the experimental
literature. The values of the free parameters of the model are estimated by an
info-geometric Bayesian maximum-likelihood inference which keeps into account
the prior distribution of the parameters. The eventual goodness of fit is
quantified by means of a distribution-independent test.
  [CONTINUES]
