Analysis of Electrophysiological Data

Master of Electrical Engineering Program Cornell University Design Project Report Project Title: ANALYSIS OF ELECTROPHYSIOLOGICAL DATA Author: Wen Fan ([email protected]) Wentao Li ([email protected]) Yiqing Li ([email protected]) Abstract: Data including respiration and neural activity of mice has been acquired in Spike2. The main task for the MEng project is processing the data with Python to extract relevant information such as number of spikes during each trial (a few seconds of recording), respiration phase and coefficient of variation for each cell. If infusion or stimulation was used, the data should be separated in before and after. The Python program is supposed to produce output, which carries information for each recording file. As a functional module, the program will be applied to electrophysiological research repeatedly. Data including respiration and neural activity of mice has been acquired in Spike2. The main task for the MEng project is processing the data with Python to extract relevant information such as number of spikes during each trial (a few seconds of recording), respiration phase and coefficient of variation for each cell. If infusion or stimulation was used, the data should be separated in before and after. The Python program is supposed to produce output, which carries information for each recording file. As a functional module, the program will be applied to electrophysiological research repeatedly. OVERVIEW In this project, we desire to analyze the data acquired in Spike2 (A multi-channel continuous data acquisition and analysis package which is applied in many fields including electrophysiology, neurophysiology, cardiovascular and respiratory studies, pharmacology). Spike2 data files can be read using NEO.IO (A platform provided with an exhaustive way of loading and saving several widely used data formats in electrophysiology) and then be processed in Python code. Relevant parameters needed to be analyzed are listed in the KEY ISSUES part. Finally, Python programs we designed will be encapsulated as different functional modules, and there is a friendly GUI also created by Python to give the users an easy access. This is a common and important approach to facilitate electrophysiological research. Our work will provide a powerful tool for later researchers to acquire the data they want.