One of the most hindering limitations in the simulation of a realistic biological system is the complexity of the model which may easily lead to a very heavy computational burden. This may seriously question the possibility of optimizing system parameters and thus hinder further developments in device configuration and optimization. In this paper we study the effects of model complexity on the accuracy of the results in the computer simulation of Transcranial Magnetic Stimulation (TMS). The method has been extensively used in the last decade as a noninvasive technique to excite neurons in the brain by inducing weak electric currents in the tissue and proved to be a very promising alternative for currently invasive treatments in the cases which the increased neurogenesis is known to be beneficial such as Parkinson's and Alzheimer's diseases. A detailed 3D model of human head has been developed by combining individual patient-based brain images and the public domain Visible Human data consisting of brain white/gray matter, CSF, skull and muscles. Finite element method (low frequency Ansoft package) is used to simulate the interaction of time-varying magnetic fields with brain tissues and compute the densities of induced currents in different areas. Models with different levels of tissue separation have been developed and tested under the same condition to investigate the effects of model complexity on the distribution of fields and induced currents inside different tissues.