Transcranial magnetic stimulation (TMS) is a noninvasive treatment for a variety of neurological and neuropsychiatric disorders by triggering a calcium response through magnetic stimulation. To understand the full effects of this treatment, researchers will often use numerical simulations to model and study the calcium response. These simulations are limited to short-time simulations of single neurons due to computational complexity, restricting their use in clinical settings. In this paper, we explore an application of physics-informed neural networks (PINNs) to accurately produce long-time simulations of neuronal responses, opening the possibility of utilizing these methods in clinical applications to directly benefit patients.