A computational model of systems consolidation: optimizing memory-guided behavior using abstracted hippocampal and cortical networks

A computational model of systems consolidation: optimizing memory-guided behavior using abstracted hippocampal and cortical networks Adam Santoro Doctor of Philosophy Graduate Department of The Institute of Medical Science University of Toronto 2015 Learning is not a isolated event, as nearly every encoding event occurs on a backdrop of previous knowledge. This idea is tightly intertwined with that of memory consolidation, which is a concept that describes the fate of memories post-encoding. After the brain encodes some episode, the encoded information undergoes a transition from hippocampal dependence to cortical dependence. Contemporary memory consolidation theories emphasize the nature of the computations that occur during this transfer, pointing to the extraction of statistical regularities, or the formation of schemas, as critical components. The work presented in this thesis analyzes memory consolidation from a computational perspective. Firstly, it describes necessary conditions for the extraction of statistical regularities across individually learned episodes, demonstrating that a consolidation-like mechanism best captures this phenomenon. Next, it probes a possible purpose of memory consolidation within a two-memory system, showing that a joint hippocampal and cortical network optimizes memory-guided behavior in a foraging-like task. Finally, some anatomical evidence is explored within the context of a major tenet of memory consolidation, namely the slow changes in interconnectivity between distributed neocortical regions during the consolidation period. The work presented in this thesis constitutes a highly comprehensive model of memory consolidation and provides a basis for further inquiry into the nuances of consolidation phenomena, such as the effects of memory interference, neurogenesis, and forgetting.

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