A Thousand Brains, by Jeff Hawkins (2021)

[Vernon] Mountcastle based his proposal for a universal algorithm on several lines of evidence. First, as I have already mentioned, is that the detailed circuits seen everywhere in the neocortex are remarkably similar. If I showed you two silicon chips with nearly identical circuit designs, it would be safe to assume that they performed nearly identical functions. The same argument applies to the detailed circuits of the neocortex. Second is that the major expansion of the modern human neocortex relative to our hominid ancestors occurred rapidly in evolutionary time, just a few million years. This is probably not enough time for multiple new complex capabilities to be discovered by evolution, but it is plenty of time for evolution to make more copies of the same thing. Third is that the function of neocortical regions is not set in stone. For example, in people with congenital blindness, the visual areas of the neocortex do not get useful information from the eyes. These areas may then assume new roles related to hearing or touch. Finally, there is the argument of extreme flexibility. Humans can do many things for which there was no evolutionary pressure. For example, our brains did not evolve to program computers or make ice cream—both are recent inventions. The fact that we can do these things tells us that the brain relies on a general-purpose method of learning.

The brain learns its model of the world by observing how its inputs change over time. There isn’t another way to learn.

A prediction occurs when a neuron recognizes a pattern, creates a dendrite spike, and is primed to spike earlier than other neurons. With thousands of distal synapses, each neuron can recognize hundreds of patterns that predict when the neuron should become active. Prediction is built into the fabric of the neocortex, the neuron.

The longer you work on a problem, the more constraints you discover and the harder it becomes to imagine a solution. The aha moments I described in this chapter were about problems that we worked on for years. Therefore, we understood these problems deeply and our list of constraints was long. The likelihood that a solution is correct increases exponentially with the number of constraints it satisfies.

This simple observation, that we perceive objects as being somewhere—not in our eyes and ears, but at some location out in the world—tells us that the brain must have neurons whose activity represents the location of every object that we perceive.

Grid cells are like the rows and columns of a paper map, but overlaid on the animal’s environment. They allow the animal to know where it is, to predict where it will be when it moves, and to plan movements.

Place cells are like the details printed in the square. Which place cells become active depends on what the rat senses at a particular location. Place cells tell the rat where it is based on sensory input, but place cells alone aren’t useful for planning movements—that requires grid cells. The two types of cells work together to create a complete model of the rat’s environment.

Humans, rats, indeed all mammals use the same mechanism for knowing our location. We all have grid cells and place cells that create models of the places we have been.

The mapping mechanisms in the neocortex are not an exact copy of ones in the old brain. Evidence suggests that the neocortex uses the same basic neural mechanisms, but it is different in several ways. It is as if nature stripped down the hippocampus and entorhinal cortex to a minimal form, made tens of thousands of copies, and arranged them side by side in cortical columns. That became the neocortex.

Grid cells and place cells in the old brain mostly track the location of one thing: the body. They know where the body is in its current environment. The neocortex, on the other hand, has about 150,000 copies of this circuit, one per cortical column. Therefore, the neocortex tracks thousands of locations simultaneously.