The subjective experience of thermal pain follows the detection and encoding
of noxious stimuli by primary afferent neurons called nociceptors. However,
nociceptor morphology has been hard to access and the mechanisms of signal
transduction remain unresolved. In order to understand how heat transducers in
nociceptors are activated in vivo, it is important to estimate the
temperatures that directly activate the skin-embedded nociceptor membrane.
Hence, the nociceptor’s temperature threshold must be estimated, which in turn
will depend on the depth at which transduction happens in the skin. Since the
temperature at the receptor cannot be accessed experimentally, such an
estimation can currently only be achieved through modeling. However, the
current state-of-the-art model to estimate temperature at the receptor suffers
from the fact that it cannot account for the natural stochastic variability of
neuronal responses. We improve this model using a probabilistic approach which
accounts for uncertainties and potential noise in system. Using a data set of
24 C-fibers recorded in vitro, we show that, even without detailed knowledge
of the bio-thermal properties of the system, the probabilistic model that we
propose here is capable of providing estimates of threshold and depth in cases
where the classical method fails