Studying coherence from extracellular and intracellular electrical recordings in hippocampal slices provides a way to uncover, characterize and clarify many tasks and functions associated with the hippocampus. In this thesis, a signal processing tool was developed to study coherence on nonstationary biological data from hippocampal slices. Time Delay Estimation was used to find the maximum likelihood of the location of the best match between the biological recordings. Continuous Wavelet Transform was employed to decompose the data into two groups of low and high frequency ranges. Multichannel Blind System Identification was applied on the grouped signals to find their common signal. Finally, coherence measures for nonstationary biological data from hippocampal slices were obtained by utilizing the stationary coherence function on sliding windows between the common signal and the recorded signals.The thesis shows that the signal processing tool can be used as coherence analysis of nonstationary biological signals from hippocampal slices.