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Abstract:
We present a neural network model that shows how the
prefrontal cortex, interacting with the basal ganglia, can
maintain a sequence of phonological information in
activation-based working memory (i.e., the
phonological loop
). The primary function of this phonological loop may be to
transiently encode arbitrary bindings of information necessary
for tasks -- the combinatorial expressive power of language
enables very flexible binding of essentially arbitrary pieces of
information. Our model takes advantage of the closed-class nature
of phonemes, which allows different neural representations of all
possible phonemes at each sequential position to be encoded. To
make this work, we suggest that the basal ganglia provide a
region-specific update signal that allocates phonemes to the
appropriate sequential coding slot. To demonstrate that flexible,
arbitrary binding of novel sequences can be supported by this
mechanism, we show that the model can generalize to novel
sequences after moderate amounts of training.
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