What is known about the information that's passed upwards within the neocortex? The neocortex is likely to process sparse representations in a hierarchy with information close to the raw sensor input appearing in lower levels and abstract concepts being appearing in higher levels. However, I'm interested in the exact nature of information being passed upwards.


There are some gating mechanisms in the neocortex. I could imagine that they prevent obvious information from being passed up. For example, since it was already reported and didn't changed. Much like workers within corporate hierarchies only report the same status once.
Similarly, it would make sense to pass up information that a lower layer doesn't understand and can't handle itself. This would also be comparable to workers at a company who ask their managers for decision they can't make.
If planning happens within the hierarchy of the neocortex, higher layers might hand down high levels options for the lower levels to unroll and simulate. In that case, the result might be passed up afterwards.
Encoding continuous input streams, attractor patterns are likely to emerge within levels of the hierarchy. I could imagine that the type of information send upwards changes when a level settles in a stable pattern.


Is there any evidence for or against these guesses? What else know about the nature of information that's passed up within the neocortex?
Your guess 1 basically sounds like habituation:

https://en.wikipedia.org/wiki/Habituation

Per your clarification in your comment, 2 sounds like you are generally talking about the role of prediction error in learning. There's a lot of work on this. Neural network models generally learn by modifying the connection strengths in response to error. The most well-known algorithm for this is known as backpropagation. Although this is an error signal propagating down a network, not up it, as you proposed. There's also the Rescorla-Wagner model of learning, another example of error-driven learning.

3 sounds like top-down processing:

https://en.wikipedia.org/wiki/Top-down_and_bottom-up_design#Neuroscience_and_psychology

Regarding 4, neural firing patterns definitely settle into attractors. This has been pretty extensively modeled:

http://www.scholarpedia.org/article/Attractor_network