Nerves are the information highway of the human body and they transfer information, such as pain or muscle movement, from one end of it to the other. It is advantageous to understand, and sometimes manipulate, neural activity for better health care. Towards that end, there has been a recent increase in the study of neuromodulation and neural recording, which are two sides of the same coin. Noninvasive electrical neuromodulation is particularly popular though it has lower spatial-resolution than other more invasive methods. Similarly, neural spike detection through thresholding is a common method of recording neural activity, though it can suffer from background noise swamping out some signals. This thesis aims to study and improve neuromodulation through the systematic approach of using a finite element model of nerves and their surrounding tissue to develop and test a method of optimizing current injected in fixed electrodes that allows for targeting different nerves. It also validates the approach used to create a PCB electrical mesh phantom of the finite element model through voltage distribution measurements using custom circuitry. Such an electrical mesh phantom would be an aid to further studies involving neuromodulation and neural recording. In an effort to overcome the limiting effect background noise has on detecting neural spikes through thresholding, the phenomenon of stochastic resonance is explored to increase the probability of detecting neural activity.