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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 are
qualitatively consistent with human performance, and we interpret
human limitations in terms of synchrony and time delays, both
biologically plausible. 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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