In the first part of the seminar I will review the main features of the Hopfield model, providing as well an overview on its numerous and broad applications. Then, inspired by biological information-processing, I will enrich the framework and make the network able to successfully and cheaply handle structured datasets. In particular, I will recast reinforcement and remotion mechanisms occurring in mammal’s brain during sleep into suitable machine-learning hyperparameters. Finally, I will show that this framework is also effective to face a classical inference problem, that is, matrix factorisation. Results presented are both analytical and computational.