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In this study, Yuichi Yamashita and Jun Tani demonstrate that even without explicit spatial hierarchical structure a, functional hierarchy can self-organize through multiple timescales in neural activity. Their model was proven viable when tested with the physical body of a humanoid robot.

Results suggest that it is not only the spatial connections between neurons, but also the timescales of neural activity, that act as important mechanisms in neural systems.[2]
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Neurons discriminate among signals based on the signals’ “shape,” (how a signal changes over time), and Forger and coauthors found that, contrary to prior belief, a neuron’s preference depends on context. Neurons are often compared to transistors on a computer, which search for and respond to one specific pattern, but it turns out that neurons are more complex than that. They can search for more than one signal at the same time, and their choice of signal depends on what else is competing for their attention.[1]

“Second, we found that the optimal stimulus is context-dependent,” he said, “so the best signal will differ, depending on the part of the brain where the implant is placed.” [1]
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To recap:
Information is data in context
Context is organized data
Learning is the self-organization of data [forms context]

So yes systems can do this, it’s based on timing. I call it occurrence based data binding[data binding in space time] and bollean is just sub-set. In other words we got the math and physical evidence to do what needs to/can be done.

1. http://www.sciencedaily.com/releases/2011/07/110708124538.htm
2. http://www.sciencedaily.com/releases/2008/11/081107071820.htm