  In this paper we continue our investigation of the analogical neural network,
paying interest to its replica symmetric behavior in the absence of external
fields of any type. Bridging the neural network to a bipartite spin-glass, we
introduce and apply a new interpolation scheme to its free energy that
naturally extends the interpolation via cavity fields or stochastic
perturbations to these models. As a result we obtain the free energy of the
system as a sum rule, which, at least at the replica symmetric level, can be
solved exactly. As a next step we study its related self-consistent equations
for the order parameters and their rescaled fluctuations, found to diverge on
the same critical line of the standard Amit-Gutfreund-Sompolinsky theory.
