A Thousand Brains: A New Theory of Intelligence

by Jeff Hawkins

The neocortex occupies about 70 percent of the volume of a human brain and it is responsible for everything we associate with intelligence, from our senses of vision, touch, and hearing, to language in all its forms, to abstract thinking such as mathematics and philosophy.

In every region they have examined, scientists have found cells that project to some part of the old brain related to movement.

Looking at the surface of the neocortex, a cortical column occupies about one square millimeter. It extends through the entire 2.5 mm thickness, giving it a volume of 2.5 cubic millimeters. By this definition, there are roughly 150,000 cortical columns stacked side by side in a human neocortex. You can imagine a cortical column is like a little piece of thin spaghetti. A human neocortex is like 150,000 short pieces of spaghetti stacked vertically next to each other.

Not knowing what dendrite spikes are for, AI researchers use simulated neurons that don’t have them. They also don’t have dendrites and the many thousands of synapses found on the dendrites. I knew that distal synapses had to play an essential role in brain function. Any theory and any neural network that did not account for 90 percent of the synapses in the brain had to be wrong.

If their preferred input arrives, they all want to start spiking. However, if one or more of the neurons are in the predictive state, our theory says, only those neurons spike and the other neurons are inhibited. Thus, when an input arrives that is unexpected, multiple neurons fire at once. If the input is predicted, then only the predictive-state neurons become active. This is a common observation about the neocortex: unexpected inputs cause a lot more activity than expected ones.

In mammals, the old brain parts where these map-creating neurons exist are called the hippocampus and the entorhinal cortex. In humans, these organs are roughly the size of a finger. There is one set on each side of the brain, near the center.

Mountcastle knew that cortical columns aren’t completely identical. There are physical differences, for example, between columns that get input from your fingers and columns that understand language, but there are more similarities than differences. Therefore, Mountcastle deduced that there must be some basic function that underlies everything the neocortex does—not just perception, but all the things we think of as intelligence.

Earlier, I posited that brains first evolved reference frames to learn the structure of environments so that we could move about the world. Then our brains evolved to use the same mechanism to learn the structure of physical objects so that we could recognize and manipulate them. I am now proposing that our brains once again evolved to use that same mechanism to learn and represent the structure underlying conceptual objects, such as mathematics and democracy.

I don’t know how long the current wave of AI will continue to grow. But I do know that deep learning does not put us on the path to creating truly intelligent machines. We can’t get to artificial general intelligence by doing more of what we are currently doing. We have to take a different approach.

don’t believe any kind of deep learning network will achieve the goal of AGI if the network doesn’t model the world the way a brain does.

How does the brain do it? The unit of processing in the neocortex is the cortical column. Each column is a complete sensory-motor system—that is, it gets inputs and it can generate behaviors. With every movement, a column predicts what its next input will be. Prediction is how a column tests and updates its model.

Neurons take at least five milliseconds to do anything useful. Transistors made of silicon can operate almost a million times faster.