The tragedies of memory impairment are well known, whether they are due to focal brain damage, neurodegenerative disease, or advanced age. The goal of the proposed research is to further our understanding of both normal and pathological memory. The applicant proposes both experimental and theoretical work within the framework of current mathematical and neural network memory models.
Specific aims of the proposed research are to further develop a model of sequence learning and to elucidate the ways in which memory for sequences is both similar and different from simple associative learning. In addition to the modeling of sequential behavior, a new theory of how time information is coded in memory is proposed. Experiments will test the predictions of this theory with the aim of gaining a better understanding of the relation between episodic and semantic memory. The proposed research uses the tools provided by mathematical and neural network memory models to better understand the fundamental processes underlying memory performance. This understanding will improve the interpretation of patterns of deficits observed when memory is impaired.
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