1. An apparatus for converting a description of a circuit into an abstract model of the circuit, the apparatus comprising: a causality engine coupled to at least one database comprising protocol information relating to the circuit, a model description of the circuit, and simulation data associated with the circuit, the causality engine configured to determine deterministic behavior between input and output signals of the simulation data; and a neural network coupled to the causality engine and configured to generate an abstract model of the circuit approximating a behavior of the circuit based on the determination of deterministic repetitive behavior by the causality engine; wherein, to generate an abstract model of the circuit, the neural network is configured to: generate a system of weighted equations representing the behavior of the circuit; apply input patterns to the system of equations to generate actual output values; calculate an error using a difference between the actual output values and desired values; and modify the weightings in the system of equations based on the calculated error.