Perceiving without Learning: From Spirals to Inside/Outside Relations

As a benchmark task, the spiral problem is well known in neural networks. Unlikeprevious 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 resultsare 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.