Generative and discriminative models use both gradient-descent based learning procedures for minimizing cost functions
However, in energy based models we don't use backpropagation and automatic differentiation for computing gradients, instead we turn to Markov Chain Monte Carlo methods.
A typical deep neural network has several hidden layers. A restricted Boltzmann machine has normally one hidden layer, however several RBMs can be stacked to make up deep Belief Networks, of which they constitute the building blocks.