This research will focus on advancing our understanding of neural dynamics and coding through the development of computational methods and experimental approaches. It focuses on three main areas: real-time detection of sharp-wave ripples in hippocampal local field potentials using Temporal Convolutional Networks, modeling nonlinear interactions in brain signals to predict neural activity and quantify nonlinearity, and investigating representational drift and the stability of spike sequences. These efforts integrate machine learning techniques, experimental tools such as 2-photon imaging with graphene-coated polymer probes, and theoretical frameworks to address fundamental questions about neural computation, memory consolidation, and representational stability.