  As well known, Hebb's learning traces its origin in Pavlov's Classical
Conditioning, however, while the former has been extensively modelled in the
past decades (e.g., by Hopfield model and countless variations on theme), as
for the latter modelling has remained largely unaddressed so far; further, a
bridge between these two pillars is totally lacking. The main difficulty
towards this goal lays in the intrinsically different scales of the information
involved: Pavlov's theory is about correlations among \emph{concepts} that are
(dynamically) stored in the synaptic matrix as exemplified by the celebrated
experiment starring a dog and a ring bell; conversely, Hebb's theory is about
correlations among pairs of adjacent neurons as summarized by the famous
statement {\em neurons that fire together wire together}. In this paper we rely
on stochastic-process theory and model neural and synaptic dynamics via
Langevin equations, to prove that -- as long as we keep neurons' and synapses'
timescales largely split -- Pavlov mechanism spontaneously takes place and
ultimately gives rise to synaptic weights that recover the Hebbian kernel.
