  In this paper we study the theory of the so-called Kantorovich max-product
neural network operators in the setting of Orlicz spaces $L^{\varphi}$. The
results here proved, extend those given by Costarelli and Vinti in Result
Math., 2016, to a more general context. The main advantage in studying neural
network type operators in Orlicz spaces relies in the possibility to
approximate not necessarily continuous functions (data) belonging to different
function spaces by a unique general approach. Further, in order to derive
quantitative estimates in this context, we introduce a suitable K-functional in
$L^{\varphi}$ and use it to provide an upper bound for the approximation error
of the above operators. Finally, examples of sigmoidal activation functions
have been considered and studied in details.
