Short Abstract — Based on Across-Neural-Pattern theory, we

planned to model the encoding and decoding processes that occur during neural activity. By preprocessing our data set into multiple resolutions based on window size and utilizing the sliding window approach to generate their values, we are learning to create a Generalize Logical Network. Our plan is to create two GLN networks; one for the encoding process and the other for the decoding process. Each network will consist of several resolution nodes and 4 stimulus nodes