We interpret the time interval data obtained from periodically stimulated sensory neurons in terms of two simple dynamical systems driven by noise with an embedded weak periodic function called the signal: 1) a bistable system defined by two potential wells separated by a barrier, and 2) a Fit(cid:173) zHugh-Nagumo system. The implementation is by analog simulation: elec(cid:173) tronic circuits which mimic the dynamics. For a given signal frequency, our simulators have only two adjustable parameters, the signal and noise intensi(cid:173) ties. We show that experimental data obtained from the periodically stimu(cid:173) lated mechanoreceptor in the crayfish tail fan can be accurately approximated by these simulations. Finally, we discuss stochastic resonance in the two models.