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Summary: Abstract. A computational model of hippocampal ac-
tivity during spatial cognition and navigation tasks is
presented. The spatial representation in our model of the
rat hippocampus is built on-line during exploration via
two processing streams. An allothetic vision-based
representation is built by unsupervised Hebbian learning
extracting spatio-temporal properties of the environ-
ment from visual input. An idiothetic representation is
learned based on internal movement-related information
provided by path integration. On the level of the
hippocampus, allothetic and idiothetic representations
are integrated to yield a stable representation of the
environment by a population of localized overlapping
CA3-CA1 place ®elds. The hippocampal spatial repre-
sentation is used as a basis for goal-oriented spatial
behavior. We focus on the neural pathway connecting
the hippocampus to the nucleus accumbens. Place cells
drive a population of locomotor action neurons in the
nucleus accumbens. Reward-based learning is applied to
map place cell activity into action cell activity. The
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