Neurons are the rudimentary building blocks of the human body which possess electrically excitable signals that carry information such as pain and muscle movement. The complexities of the human brain are widely studied in various spike detection and sorting algorithms by analyzing the neural activity. To diversify the understanding and analysis of neural signals, an accurate and precise synthetic data set must be generated. This thesis presents a computational tool that accurately models the neural environment while incorporating the extracellular low-pass frequency filtering and cortical layer inhomogeneity effects – both which are major influencers for changing the extracellular spike waveform shape. The characteristics and implementation results of this design are compared against some of the state-of-the-art simulation tools for generating synthetic neural data sets to validate and distinguish this tool for aiding in further studies of biological neural networks or spike detection and sorting algorithms.