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Research Report A reinforcement learning approach to model interactions
 

Summary: Research Report
A reinforcement learning approach to model interactions
between landmarks and geometric cues during
spatial learning
Denis Sheynikhovich, Angelo Arleo
UPMC-Paris 6, CNRS-UMR7102, 9 quai St. Bernard, F-75005 Paris, France
A R T I C L E I N F O A B S T R A C T
Article history:
Accepted 26 September 2010
In contrast to predictions derived from the associative learning theory, a number of
behavioral studies suggested the absence of competition between geometric cues and
landmarks in some experimental paradigms. In parallel to these studies, neurobiological
experiments suggested the existence of separate independent memory systems which may
not always interact according to classic associative principles. In this paper we attempt to
combine these two lines of research by proposing a model of spatial learning that is based on
the theory of multiple memory systems. In our model, a place-based locale strategy uses
activities of modeled hippocampal place cells to drive navigation to a hidden goal, while a
stimulus­response taxon strategy, presumably mediated by the dorso-lateral striatum,
learns landmark-approaching behavior. A strategy selection network, proposed to reside in
the prefrontal cortex, implements a simple reinforcement learning rule to switch behavioral

  

Source: Arleo, Angelo - Laboratory of Neurobiology of Adaptive Processes, Université Pierre-et-Marie-Curie, Paris 6

 

Collections: Biology and Medicine