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Adam Johnson

 

 

Wednesday 2nd July 2014

Time: 4pm

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

A hierarchical Bayesian approach to hippocampal based schema learning and exploration

The hippocampus plays a critical role in spatial look-ahead, single-trial learning, memory consolidation, and imagination. Each of these learning dynamics depends on memory schemas. Using a hierarchical Bayesian approach, we propose a computational definition for memory schemas as dynamic hyperparameter mixtures used to predict future task observations. We show how this approach can account for the hippocampus
dependence of single trial learning and variable time memory consolidation. We next show how this approach can account for spontaneous object exploration behavior. Finally, we discuss the links between this approach and POMDP approaches that emphasize the role of memory in problem solving rather than memory as purely information
storage.