  A formulation of Langevin dynamics for discrete systems is derived as a class
of generic stochastic processes. The dynamics simplify for a two-state system
and suggest a network architecture which is implemented by the Langevin
machine. The Langevin machine represents a promising approach to compute
successfully quantitative exact results of Boltzmann distributed systems by LIF
neurons. Besides a detailed introduction of the dynamics, different simplified
models of a neuromorphic hardware system are studied with respect to a control
of emerging sources of errors.
