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Abstract:
We present a model of the hippocampus dependent performance
of rats in a delayed match-to-place spatial watermaze learning
task. Real rats show slow learning in the first few days of
training; but ultimately exhibit one-trial learning. We model the
early performance using a temporal difference (TD) reinforcement
learning architecture, using place cells in the hippocampus as the
representation of space. We model late performance using a
coordinate learning system which also employs TD learning and place
cells. We show a simple integration of these two navigation
models.
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