  We are assisting at a growing interest in the development of learning
architectures with application to digital communication systems. Herein, we
consider the detection/decoding problem. We aim at developing an optimal neural
architecture for such a task. The definition of the optimal criterion is a
fundamental step. We propose to use the mutual information (MI) of the channel
input-output signal pair, which yields to the minimization of the a-posteriori
information of the transmitted codeword given the communication channel output
observation. The computation of the a-posteriori information is a formidable
task, and for the majority of channels it is unknown. Therefore, it has to be
learned. For such an objective, we propose a novel neural estimator based on a
discriminative formulation. This leads to the derivation of the mutual
information neural decoder (MIND). The developed neural architecture is capable
not only to solve the decoding problem in unknown channels, but also to return
an estimate of the average MI achieved with the coding scheme, as well as the
decoding error probability. Several numerical results are reported and compared
with maximum a-posteriori and maximum likelihood decoding strategies.
