Since information in the brain is processed by the exchange of spikes among neurons a study of
such group dynamics is extremely important in understanding hippocampus dependent memory. These
spike patterns and local field potentials (LFPs) have been analyzed by various statistical
methods. These studies have led to important findings of memory information processing. For
example memory-trace replay a reactivation of behaviorally induced neural patterns during
subsequent sleep has been suggested to play an important role in memory consolidation. It has
also been suggested that a ripple sharp wave event (one of the characteristics of LFPs in the
hippocampus) and spiking activity in the cortex have a specific relationship that may
facilitate the consolidation of hippocampal dependent memory from the hippocampus to the
cortex. The book will provide a state-of-the-art finding of memory information processing
through the analysis of multi-neuronal data. The first half of the book is devoted to this
analysis aspect. Understanding memory information representation and its consolidation however
cannot be achieved only by analyzing the data. It is extremely important to construct a
computational model to seek an underlying mathematical principle. In other words an entire
picture of hippocampus dependent memory system would be elucidated through close collaboration
among experiments data analysis and computational modeling. Not only does computational
modeling benefit the data analysis of multi-electrode recordings but it also provides useful
insight for future experiments and analyses. The second half of the book will be devoted to the
computational modeling of hippocampus-dependent memory.
