Veridical Suppression
Instead of representing reality in precise detail, a Grassland node models an interface from its environment in manner similar to the way humans themselves model an interface for their environment.
When navigating through a busy store to find a product, it isn't necessary for the human mind to count every light fixture, every tile, every shelf, every pane of glass etc etc. However, a camera will capture these things. Pointed at a brick wall inside the store, it will capture every single brick. And while the mind of the average person navigating through a store doesn't capture every single brick on wall behind the shelves, despite being directly in its field of view but a camera does, yet the human mind is what we'd consider to be the most powerful computer in the world, at least much more powerful than a digital camera.
The human mind is so powerful, not because of what it captures, but because of what it knows to ignore. In order to walk down a busy street, one only need keep track of the people and vehicles within your close vicinity. The mind doesn't need to capture and remember the type of shoes everyone was wearing so that visual data is suppressed. It doesn't need to know every pedestrian's favorite song. It just needs to recognize that an object is a person, an animal or a vehicle etc. as well as their rough dimensions and velocity in order to safely navigate around them. Who they are or where they come from isn't important in this context.
Upon entering a house party, once the mind has established where the important details of the background environment are, for instance the living room, the kitchen, the bathroom, the couch and chairs etc. the mind tends to set them in place and they fade into the background. This allows the mind to focus its all its data processing power on the person in front of them and the topic of conversation. If some new extraneous information needs to be noticed, updated or corrected, perhaps their conversation partner sets their drink down on an end table they hadn't noticed of yet, the mind set it in place and then stops actively capturing it and just resigns it to the "background".
It's this sort of "set-it-and-forget-it" algorithm that Grassland employs through various means, such as using OpenStreetMaps for its background's framework in order to concentrate as much of each node's processing power on the salient information alone.
With each successive eon, the amount of salient information increases. And these eon's aren't arbitrarily ordered. Each one is a foundation necessary for next one by ensuring that each neural the greatest amount of universally available information both in terms of geography and time, The neural network updates are ordered in a necessary fashion. By combining salient, human and vehicle data. They are ordered in such a way in order to combine both.