Stochastically fluctuating multi-well systems as physical implementations of energy-based machine learning models promise a route towards neuromorphic hardware. Understanding the response of multi-well systems to dynamic input signals is crucial in this regard. Here, we investigate the stochastic response of binary orbital memory states derived from individual Fe and Co atoms on a black phosphorus surface to sinusoidal input voltages. Using scanning tunneling microscopy, we quantify the state residence times for DC and AC voltage drive with various input frequencies. We find that Fe and Co atoms both exhibit features of synchronization to the AC input, but only Fe atoms demonstrate a significant frequency-dependent change in the time-averaged state occupations. By modeling the underlying stochastic process, we show that the frequency response of the system is directly related to the DC voltage dependence of the state asymmetry. This relation provides a tunable way to induce population changes in stochastic systems and lays the foundation for understanding the response of multi-well systems to dynamical input signals.
