Sounds like you are working on the same types of problems as we are. If we make progress on these issues we will be sure to share and compare notes. In case this helps, here are some ideas I/we am working on…just ideas, nothing set yet.
- An object is comprised of a set of sub-objects. These are represented by a set of displacement vectors.
- L2/3 projects to L5 and back again. I like the idea that L2/3 is a stable representation of an object which invokes a union of displacement vectors in L5. So L2/3 is an object and L5 is the actual definition of the object, meaning a set of sub-objects are relative positions to each other.
- By invoking an object representation in L2/3 you invoke a union of sub-objects L5
- Similarly, when you observe a novel object, you serially attend to different parts, building a union of displacement vectors. This union will invoke activity in L2/3 of any objects that had similar parts at similar relationships.
- Your conscious perception is of an object in a particular state. I perceive an open stapler or a closed stapler, not just “stapler”. This suggests that L2/3 is actually a stable representation of the object in a particular state.
- This brings up a problem. If L2/3 is an object in a particular state then how do I know the open and closed stapler are really the same object? I can think of one possible answer.
- Perhaps L2 represents the generic object and L3 represents the object in a particular state. L2 pools over the states in L3. We perceive L3 (which makes sense because L3 is passed to higher regions). L2 and L3 can be used for column to column voting.
L2 = base object, best for classifying, pools over possible object states in L3
L3 = object in particular state, what we perceive, invokes the correct union of displacements in L5 for the current state of the object, pools over Lj4
L5 = union of displacements appropriate to the current object state
Behaviors could be learned as a sequence L5 or perhaps in L3.