Perceiving without learning: from spirals to inside/outside relations

As a benchmark task, the spiral problem is well known in neural net-works. 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 in-side/outside problem. A generic solution to both problems is proposed based on oscillatory correlation using a time delay network. Our simu-lation 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.