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.

The probabilistic model results to be accurate with high statistical significance, thus confirming the theoretical prediction of a power-law dependence on odorant concentration. The experimental data available about the distribution of sensitivities also agree with the other predictions, though they are not statistically sufficient for a very stringent verification. Furthermore, the theory suggests a potential evolutionary reason for the exponent of the sensitivity power-law to be significantly different from the unit. The power-law dependence on concentration is consistent with the psychophysical Stevens Law.

On the whole, from the formalisation of just a few phenomenological observations a compact model is derived that may fit experimental findings from several levels of research on olfaction.

根据神经嗅觉系统评估气味刺激强度的明显能力，同时还可以在较大浓度范围内识别气味剂的身份，在一些潜在的神经元上具有生物学上的现实假设流程。尤其是，假定受体神经元是指具有气味强度的单调激发速率标度，并且受体灵敏度在气味剂和受体神经元之间的分布范围很广，因此导致了刺激的高度分布表示。现象学假设的数学实现允许推断一些可测量量之间的显式功能关系。

为了定量测试人口平均燃烧速率对气味浓度的幂律依赖性的预测，创建了一个概率模型以从实验文献中可获得的数据中提取信息。通过信息几何贝叶斯最大似然推断来估计模型的自由参数的值，该推断考虑了参数的先验分布。最终的拟合优度通过独立于分布的测试进行量化。

概率模型的结果是准确的，具有很高的统计意义，因此证实了幂律对气味剂浓度的依赖性的理论预测。关于敏感性分布的可用实验数据也与其他预测一致，尽管它们在统计上不足以进行非常严格的验证。此外，该理论提出了灵敏度幂律指数与单位显着不同的潜在进化原因。权力定律对集中力的依赖与心理物理学史蒂文斯定律是一致的。

总体而言，仅通过少量现象学观察的形式化，便得出了一个紧凑的模型，该模型可能适合于嗅觉研究各个级别的实验结果。