Synchronization of neural signals is important for function of the nervous system, and the disruption of synchronized or phase locked network activity is implicated in disorders like schizophrenia. In this proposal, we use two-cell computational models and hybrid circuits of one biological and one model neuron to investigate synchronization and phase locking in small networks. We propose to characterize period drift in biological neurons to determine how robust circuits must be to counteract this drift and to identify how coupled system dynamics change in the presence of biological drift. We will quantify how the number of fixed points in the coupled system affects the network's ability to withstand perturbations. Finally, we will determine how the shape of the system's interaction curves and the number of system fixed points affect the stability of synchronized or phase locked behavior and the convergence time back to this phase relationship after perturbation. Understanding the conditions conducive to network synchrony will give us a general framework for how networks establish and destroy oscillatory activity; such activity is thought to be important for the creation and retrieval of memories, and may give insight into diseases that possess characteristic disruptions in phase locked or synchronous activity.