I wouldn’t use the term “exponentially”, because N (the number of actively communicating nodes) isn’t in the exponent of the formula. Perhaps “quadratically” is a better (if not perfect) fit.
In any event, we clearly need to keep all of the LMs from trying to communicate with each other. So, here’s some complete Sci-Fi…
Start by using Jeff’s flattened (“dinner napkin”) visualization of the cortex. Separate this into layers (e.g., L1 … L6), then add layers for supporting actors. Borrowing from my “Mermaid Musings” thread, we might get something like this:
MH Motor Hardware (e.g., electric motor)
MD Motor Drivers (handle output geometry, etc.)
MM Motor Modules (map from CMP into geometry)
LM (in L1 ... L6) (model known cortical layers)
SM Sensor Modules (map from geometry into CMP)
SD Sensor Drivers (handle input geometry, etc.)
SH Sensor Hardware (e.g., RGBD digital camera)
To bootstrap the system, we’d supply the actors with hints about default connectivity and activity, as:
- Each SM should converse with the “relevant” SDs, getting data for a small region around a particular part of the digitized image.
- Each LM in L1 … L6 should request and receive data from the relevant SMs, as well as “neighboring” LMs.
- …
Then, while the system is operating, we’d (somehow) add more hints concerning long-range connections. (ducks)