We present our ongoing project aimed at understanding the neural basis of complex behaviors and temporal organization of memories. More specifically, we focus on a unique electrophysiological experiment designed to address fundamental and unresolved questions about hippocampal function. Our goal is to elucidate the neural mechanisms underlying the memory for sequences of events, a defining feature of episodic memory. To this end, we have developed a flexible Bayesian framework for dynamic modeling of brain connectivity. Using the Cholesky decomposition, our method presents the correlation matrix as the product of unit spheres with increasing dimensions. The sphere-product representation is amenable for the inferential algorithm to handle the resulting intractability, and hence lays the foundation for full flexibility in choosing priors. The proposed model, however, lacks scalability for high dimensional problems. To address this issue, we propose a latent factor Gaussian process model. We show that this approach could lead to unprecedented insight into the neural mechanisms underlying memory impairments.