There are three phenomena in the brain that fascinate me: chunking, magnification, and cortical remapping. I’m not sure if all of them can be ascribed to activity within the neocortex, but from my limited understanding, I think this is so.
I’m still new myself, but I believe chunking can be explained within HTM. Thoughts and inputs are represented as SDRs moving up and down the hierarchy. If a given collection of concepts are presented simultaneously often enough, a subset of minicolumns (neurons) would become sensitive to that collection, and thereafter those neurons would represent the new chunk.
I can’t answer most of your question but I will tackle the chunking part. It is an emergent property of system level operation.
The current “contents of consciousness” - the collection of interlocking maps in the brain, each holding some part of the parsed here and now; a basket of interlocking features that combine to describe some unique collection that we take as a chunk. You have two halves of your brain that mostly are duplicates of each other and it does not take the entire brain to form a content. Humans seem to be stuck at about 4 or so simultaneous patterns before they interfere with each other. I think that this is related to how we process tuples in the loop of consciousness.
I take is as a matter of faith that the brain processes tuples serially.
(Tuple = object-relationship-object)
Thing one is recalled/perceived and the loop of consciousness projects thing two with some relationship object to be perceived, evolving to thing three.
See “loop of consciousness”:
See “contents of consciousness”:
and the relation of the “contents of consciousness” and long term memory:
What is the logic of learning exceptions? I ask myself.
1/ Exceptions are rarer than the more general thing. By chance you will generally learn them later. You capture the conditional probabilities in a crude way.
2/ There is a process of inclusion and exclusion going on. You learn an exception but it is likely too broad and you must learn what to exclude from that exception.
3/ You get a list of alternative answers by following the chain of exceptions. You don’t have to fully accept what was seen in the data at the most extreme exception.
4/ You may choose only to add an exception only after it has been encountered a number of times. Like synapse building only after a number of repeat events.
5/ You can extract sequences of exceptions as trigger patterns, that maybe you can do further learning from. As in going from letter representations to word representations.