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Perceiving without Learning: from Spirals to Inside/Outside Relations
ABSTRACT
As a benchmark task, the spiral problem is well known in neural networks.
Unlike previous work that emphasizes learning, we approach the problem from
a generic perspective that does not involve learning. We point out that the
spiral problem is intrinsically connected to the inside/outside problem.
A generic solution to both problems is proposed based on oscillatory
correlation using a time delay network. Our simulation results could be
used to interpret human limitations from a biologically plausible standpoint.
As a special case, our network without time delays can always distinguish
these figures regardless of shape, position, size, and orientation. We
conjecture that visual perception will be effortful if local activation cannot
be rapidly propagated, as synchrony would not be established in the presence
of time delays.
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