Restricted Boltzmann Machines (RBM) are neural network models that learn a probability distribution and a representation of data, they belong to the class of Boltzmann Machines. With respect to the lasts, in RBM the learning procedure is simpler and faster. Furthermore, once properly trained, final couplings will directly show the main correlations between visible units. In this work we apply RBM to a specific cortical neural data set and demonstrate its usefulness in revealing important properties of this brain region. Starting from the studies of A. Peyrache et al. on medial Prefrontal Cortex's neural activity in a rat, in which, using simple PCA, a cell assembly coding for a particular learned rule was found, we tried to reproduce their result, deepen the description of such phenomenon.