The two parameters of a Generative Stochastic Network (GSN) are:

1. $p(\bold x^{(k)} | \bold h^{(k)})$, which defines how to generate the next visible variable given the current latent state. This process is similar to reconstruction in Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep Boltzmann Machines (DBMs).
2. $p(\bold h^{(k)} | \bold h^{(k-1)}, \bold x^{(k-1)})$, which defines how to update the current latent variable state given the previous latent and visible variable states.